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Given the following machine learning model name: Macaw, provide a description of the model | **Macaw** is a generative question-answering (QA) system that is built on UnifiedQA, itself built on [T5](https://paperswithcode.com/method/t5). Macaw has three interesting features. First, it often produces high-quality answers to questions far outside the domain it was trained on, sometimes surprisingly so. Second, M... |
Given the following machine learning model name: Tanh Activation, provide a description of the model | **Tanh Activation** is an activation function used for neural networks:
$$f\left(x\right) = \frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$
Historically, the tanh function became preferred over the [sigmoid function](https://paperswithcode.com/method/sigmoid-activation) as it gave better performance for multi-layer neura... |
Given the following machine learning model name: Non-linear Independent Component Estimation, provide a description of the model | **NICE**, or **Non-Linear Independent Components Estimation** is a framework for modeling complex high-dimensional densities. It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is l... |
Given the following machine learning model name: Detailed Expression Capture and Animation, provide a description of the model | **Detailed Expression Capture and Animation**, or **DECA**, is a model for 3D face reconstruction that is trained to robustly produce a UV displacement map from a low-dimensional latent representation that consists of person-specific detail parameters and generic expression parameters, while a regressor is trained to p... |
Given the following machine learning model name: GFP-GAN, provide a description of the model | **GFP-GAN** is a generative adversarial network for blind face restoration that leverages a generative facial prior (GFP). This Generative Facial Prior (GFP) is incorporated into the face restoration process via channel-split spatial feature transform layers, which allow for a good balance between realness and fidelity... |
Given the following machine learning model name: IoU-Balanced Sampling, provide a description of the model | **IoU-Balanced Sampling** is hard mining method for object detection. Suppose we need to sample $N$ negative samples from $M$ corresponding candidates. The selected probability for each sample under random sampling is:
$$ p = \frac{N}{M} $$
To raise the selected probability of hard negatives, we evenly split the ... |
Given the following machine learning model name: TinaFace, provide a description of the model | **TinaFace** is a type of face detection method that is based on generic object detection. It consists of (a) Feature Extractor: [ResNet](https://paperswithcode.com/method/resnet)-50 and 6 level [Feature Pyramid Network](https://www.paperswithcode.com/method/fpn) to extract the multi-scale features of input image; (b) ... |
Given the following machine learning model name: Four-dimensional A-star, provide a description of the model | The aim of 4D A* is to find the shortest path between two four-dimensional (4D) nodes of a 4D search space - a starting node and a target node - as long as there is a path. It achieves both optimality and completeness. The former is because the path is shortest possible, and the latter because if the solution exists th... |
Given the following machine learning model name: HS-ResNet, provide a description of the model | **HS-ResNet** is a [convolutional neural network](https://paperswithcode.com/methods/category/convolutional-neural-networks) that employs [Hierarchical-Split Block](https://paperswithcode.com/method/hierarchical-split-block) as its central building block within a [ResNet](https://paperswithcode.com/method/resnet)-like ... |
Given the following machine learning model name: BiGG, provide a description of the model | **BiGG** is an autoregressive model for generative modeling for sparse graphs. It utilizes sparsity to avoid generating the full adjacency matrix, and reduces the graph generation time complexity to $O(((n + m)\log n)$. Furthermore, during training this autoregressive model can be parallelized with $O(\log n)$ synchron... |
Given the following machine learning model name: Hamburger, provide a description of the model | **Hamburger** is a global context module that employs matrix decomposition to factorize the learned representation into sub-matrices so as to recover the clean low-rank signal subspace. The key idea is, if we formulate the inductive bias like the global context into an objective function, the optimization algorithm to ... |
Given the following machine learning model name: Implicit PointRend, provide a description of the model | **Implicit PointRend** is a modification to the [PointRend](https://paperswithcode.com/method/pointrend) module for instance segmentation. Instead of a coarse mask prediction used in [PointRend](https://paperswithcode.com/method/pointrend) to provide region-level context to distinguish objects, for each object Implicit... |
Given the following machine learning model name: SNet, provide a description of the model | **SNet** is a convolutional neural network architecture and object detection backbone used for the [ThunderNet](https://paperswithcode.com/method/thundernet) two-stage object detector. SNet uses ShuffleNetV2 basic blocks but replaces all 3×3 depthwise convolutions with 5×5 depthwise convolutions. |
Given the following machine learning model name: Adaptive Label Smoothing, provide a description of the model | |
Given the following machine learning model name: Charformer, provide a description of the model | **Charformer** is a type of [Transformer](https://paperswithcode.com/methods/category/transformers) model that learns a subword tokenization end-to-end as part of the model. Specifically it uses [GBST](https://paperswithcode.com/method/gradient-based-subword-tokenization) that automatically learns latent subword repres... |
Given the following machine learning model name: Relativistic GAN, provide a description of the model | A **Relativistic GAN** is a type of generative adversarial network. It has a relativistic discriminator which estimates the probability that the given real data is more realistic than a randomly sampled fake data. The idea is to endow GANs with the property that the probability of real data being real ($D\left(x\_{r}\r... |
Given the following machine learning model name: Fast Voxel Query, provide a description of the model | **Fast Voxel Query** is a module used in the [Voxel Transformer](https://paperswithcode.com/method/votr) 3D object detection model implementation of self-attention, specifically Local and Dilated Attention. For each querying index $v\_{i}$, an attending voxel index $v\_{j}$ is determined by Local and Dilated Attention.... |
Given the following machine learning model name: Jukebox, provide a description of the model | **Jukebox** is a model that generates music with singing in the raw audio domain. It tackles the long context of raw audio using a multi-scale [VQ-VAE](https://paperswithcode.com/method/vq-vae) to compress it to discrete codes, and modeling those using [autoregressive Transformers](https://paperswithcode.com/methods/ca... |
Given the following machine learning model name: Pyramid Pooling Module, provide a description of the model | A **Pyramid Pooling Module** is a module for semantic segmentation which acts as an effective global contextual prior. The motivation is that the problem of using a convolutional network like a [ResNet](https://paperswithcode.com/method/resnet) is that, while the receptive field is already larger than the input image, ... |
Given the following machine learning model name: WaveGrad, provide a description of the model | **WaveGrad** is a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the... |
Given the following machine learning model name: Instance Normalization, provide a description of the model | **Instance Normalization** (also known as contrast normalization) is a normalization layer where:
$$
y_{tijk} = \frac{x_{tijk} - \mu_{ti}}{\sqrt{\sigma_{ti}^2 + \epsilon}},
\quad
\mu_{ti} = \frac{1}{HW}\sum_{l=1}^W \sum_{m=1}^H x_{tilm},
\quad
\sigma_{ti}^2 = \frac{1}{HW}\sum_{l=1}^W \sum_{... |
Given the following machine learning model name: DeepCluster, provide a description of the model | **DeepCluster** is a self-supervision approach for learning image representations. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update
the weights of the network |
Given the following machine learning model name: Stochastic Regularized Majorization-Minimization, provide a description of the model | |
Given the following machine learning model name: Eligibility Trace, provide a description of the model | An **Eligibility Trace** is a memory vector $\textbf{z}\_{t} \in \mathbb{R}^{d}$ that parallels the long-term weight vector $\textbf{w}\_{t} \in \mathbb{R}^{d}$. The idea is that when a component of $\textbf{w}\_{t}$ participates in producing an estimated value, the corresponding component of $\textbf{z}\_{t}$ is bumpe... |
Given the following machine learning model name: TAPAS, provide a description of the model | **TAPAS** is a weakly supervised question answering model that reasons over tables without generating logical forms. TAPAS predicts a minimal program by selecting a subset of the table cells and a possible aggregation operation to be executed on top of them. Consequently, TAPAS can learn operations from natural languag... |
Given the following machine learning model name: Dynamic Algorithm Configuration, provide a description of the model | Dynamic algorithm configuration (DAC) is capable of generalizing over prior optimization approaches, as well as handling optimization of hyperparameters that need to be adjusted over multiple time-steps.
Image Source: [Biedenkapp et al.](http://ecai2020.eu/papers/1237_paper.pdf) |
Given the following machine learning model name: Direct Feedback Alignment, provide a description of the model | |
Given the following machine learning model name: Forward gradient, provide a description of the model | Forward gradients are unbiased estimators of the gradient $\nabla f(\theta)$ for a function $f: \mathbb{R}^n \rightarrow \mathbb{R}$, given by $g(\theta) = \langle \nabla f(\theta) , v \rangle v$.
Here $v = (v_1, \ldots, v_n)$ is a random vector, which must satisfy the following conditions in order for $g(\theta)$ ... |
Given the following machine learning model name: Variational Trace Distance Estimation, provide a description of the model | **Variational Trace Distance Estimation**, or **VTDE**, is a variational algorithm for trace norm estimation that only involves one ancillary qubit. Notably, the cost function in VTDE gathers information from a single-qubit observable and thus could avoid the barren plateau issue with logarithmic depth parameterized ci... |
Given the following machine learning model name: MNN, provide a description of the model | **Mobile Neural Network (MNN)** is a mobile inference engine tailored to mobile applications. The contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2) delivering thorough kernel optimization on operators to achieve optimal computation performanc... |
Given the following machine learning model name: Adversarially Learned Inference, provide a description of the model | **Adversarially Learned Inference (ALI)** is a generative modelling approach that casts the learning of both an inference machine (or encoder) and a deep directed generative model (or decoder) in an GAN-like adversarial framework. A discriminator is trained to discriminate joint samples of the data and the correspondin... |
Given the following machine learning model name: COLA, provide a description of the model | **COLA** is a self-supervised pre-training approach for learning a general-purpose representation of audio. It is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from different record... |
Given the following machine learning model name: Shifted Softplus, provide a description of the model | **Shifted Softplus** is an activation function ${\rm ssp}(x) = \ln( 0.5 e^{x} + 0.5 )$, which [SchNet](https://paperswithcode.com/method/schnet) employs as non-linearity throughout the network in order to obtain a smooth potential energy surface. The shifting ensures that ${\rm ssp}(0) = 0$ and improves the convergence... |
Given the following machine learning model name: Local Patch Interaction, provide a description of the model | **Local Patch Interaction**, or **LPI**, is a module used for the [XCiT layer](https://paperswithcode.com/method/xcit-layer) to enable explicit communication across patches. LPI consists of two [depth-wise 3×3 convolutional layers](https://paperswithcode.com/method/depthwise-convolution) with [Batch Normalization](http... |
Given the following machine learning model name: Feedback Memory, provide a description of the model | **Feedback Memory** is a type of attention module used in the [Feedback Transformer](https://paperswithcode.com/method/feedback-transformer) architecture. It allows a [transformer](https://paperswithcode.com/method/transformer) to to use the most abstract representations from the past directly as inputs for the current... |
Given the following machine learning model name: MobileDet, provide a description of the model | **MobileDet** is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient. This helps boost the latency-accuracy trade-off for object detection ... |
Given the following machine learning model name: Softplus, provide a description of the model | **Softplus** is an activation function $f\left(x\right) = \log\left(1+\exp\left(x\right)\right)$. It can be viewed as a smooth version of [ReLU](https://paperswithcode.com/method/relu). |
Given the following machine learning model name: ThunderNet, provide a description of the model | **ThunderNet** is a two-stage object detection model. The design of ThunderNet aims at the computationally expensive structures in state-of-the-art two-stage detectors. The backbone utilises a [ShuffleNetV2](https://paperswithcode.com/method/shufflenet-v2) inspired network called [SNet](https://paperswithcode.com/metho... |
Given the following machine learning model name: LeNet, provide a description of the model | **LeNet** is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset. The architectural design served as inspiration for future networks such as [AlexNet](https://paperswithcode.com/method... |
Given the following machine learning model name: Point Gathering Network, provide a description of the model | **PGNet** is a point-gathering network for reading arbitrarily-shaped text in real-time. It is a single-shot text spotter, where the pixel-level character classification map is learned with proposed PG-CTC loss avoiding the usage of character-level annotations. With a PG-CTC decoder, we gather high-level character clas... |
Given the following machine learning model name: Large-scale spectral clustering, provide a description of the model | # [Spectral Clustering](https://paperswithcode.com/method/spectral-clustering)
Spectral clustering aims to partition the data points into $k$ clusters using the spectrum of the graph Laplacians
Given a dataset $X$ with $N$ data points, spectral clustering algorithm first constructs similarity matrix ${W}$, where $... |
Given the following machine learning model name: 1-bit LAMB, provide a description of the model | **1-bit LAMB** is a communication-efficient stochastic optimization technique which introduces a novel way to support adaptive layerwise learning rates even when communication is compressed. Learning from the insights behind [1-bit Adam](https://paperswithcode.com/method/1-bit-adam), it is a a 2-stage algorithm which u... |
Given the following machine learning model name: MoGA-A, provide a description of the model | **MoGA-A** is a convolutional neural network optimized for mobile latency and discovered via Mobile GPU-Aware (MoGA) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). The basic building block is MBConvs (inverted residual blocks) from [MobileNetV2](https://paperswithcode.com/me... |
Given the following machine learning model name: Stacked Hourglass Network, provide a description of the model | **Stacked Hourglass Networks** are a type of convolutional neural network for pose estimation. They are based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. |
Given the following machine learning model name: Dual Multimodal Attention, provide a description of the model | In image inpainting task, the mechanism extracts complementary features from the word embedding in two paths by reciprocal attention, which is done by comparing the descriptive text and complementary image areas through reciprocal attention. |
Given the following machine learning model name: Adaptive Spline Activation Function, provide a description of the model | Stefano Guarnieri, Francesco Piazza, and Aurelio Uncini
"Multilayer Feedforward Networks with Adaptive Spline Activation Function,"
IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 3, MAY 1999
Abstract — In this paper, a new adaptive spline activation function neural network (ASNN) is presented. Due to the AS... |
Given the following machine learning model name: Effective Squeeze-and-Excitation Block, provide a description of the model | **Effective Squeeze-and-Excitation Block** is an image model block based on squeeze-and-excitation, the difference being that one less FC layer is used. The authors note the SE module has a limitation: channel information loss due to dimension reduction. For avoiding high model complexity burden, two FC layers of the S... |
Given the following machine learning model name: SSD, provide a description of the model | **SSD** is a single-stage object detection method that discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces a... |
Given the following machine learning model name: Rank Flow Embedding, provide a description of the model | |
Given the following machine learning model name: Meta-augmentation, provide a description of the model | **Meta-augmentation** helps generate more varied tasks for a single example in meta-learning. It can be distinguished from data augmentation in classic machine learning as follows. For data augmentation in classical machine learning, the aim is to generate more varied examples, within a single task. Meta-augmentation h... |
Given the following machine learning model name: SRU++, provide a description of the model | **SRU++** is a self-attentive recurrent unit that combines fast recurrence and attention for sequence modeling, extending the [SRU](https://www.paperswithcode.com/method/sru) unit. The key modification of SRU++ is to incorporate more expressive non-linear operations into the recurrent network. Specifically, given the i... |
Given the following machine learning model name: Global Convolutional Network, provide a description of the model | A **Global Convolutional Network**, or **GCN**, is a semantic segmentation building block that utilizes a large kernel to help perform classification and localization tasks simultaneously. It can be used in a [FCN](https://paperswithcode.com/method/fcn)-like structure, where the [GCN](https://paperswithcode.com/method/... |
Given the following machine learning model name: Recurrent Entity Network, provide a description of the model | The **Recurrent Entity Network** is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data. For language understanding tasks, it can reason on-the-fly as it reads text, not just when it is required to answer a question or respon... |
Given the following machine learning model name: Fixed Factorized Attention, provide a description of the model | **Fixed Factorized Attention** is a factorized attention pattern where specific cells summarize previous locations and propagate that information to all future cells. It was proposed as part of the [Sparse Transformer](https://paperswithcode.com/method/sparse-transformer) architecture.
A self-attention layer maps ... |
Given the following machine learning model name: End-to-End Neural Diarization, provide a description of the model | **End-to-End Neural Diarization** is a neural network for speaker diarization in which a neural network directly outputs speaker diarization results given a multi-speaker recording. To realize such an end-to-end model, the speaker diarization problem is formulated as a multi-label classification problem and a permutati... |
Given the following machine learning model name: ESPNet, provide a description of the model | **ESPNet** is a convolutional neural network for semantic segmentation of high resolution images under resource constraints. ESPNet is based on a convolutional module, efficient spatial pyramid ([ESP](https://paperswithcode.com/method/esp)), which is efficient in terms of computation, memory, and power. |
Given the following machine learning model name: Network Embedding as Matrix Factorization:, provide a description of the model | |
Given the following machine learning model name: SpatialDropout, provide a description of the model | **SpatialDropout** is a type of [dropout](https://paperswithcode.com/method/dropout) for convolutional networks. For a given [convolution](https://paperswithcode.com/method/convolution) feature tensor of size $n\_{\text{feats}}$×height×width, we perform only $n\_{\text{feats}}$ dropout
trials and extend the dropout va... |
Given the following machine learning model name: Gradual Self-Training, provide a description of the model | Gradual self-training is a method for semi-supervised domain adaptation. The goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain.
This comes up for example in applications ranging from sensor networks and self-driv... |
Given the following machine learning model name: BigBird, provide a description of the model | **BigBird** is a [Transformer](https://paperswithcode.com/method/transformer) with a sparse attention mechanism that reduces the quadratic dependency of self-attention to linear in the number of tokens. BigBird is a universal approximator of sequence functions and is Turing complete, thereby preserving these properties... |
Given the following machine learning model name: ALDEN, provide a description of the model | **ALDEN**, or **Active Learning with DivErse iNterpretations**, is an active learning approach for text classification. With local interpretations in DNNs, ALDEN identifies linearly separable regions of samples. Then, it selects samples according to their diversity of local interpretations and queries their labels.
... |
Given the following machine learning model name: Continuously Indexed Domain Adaptation, provide a description of the model | **Continuously Indexed Domain Adaptation** combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution.
Image Source: [Wang et al.](https://arxiv.org/pdf/2007.01807v2.pdf) |
Given the following machine learning model name: RAdam, provide a description of the model | **Rectified Adam**, or **RAdam**, is a variant of the [Adam](https://paperswithcode.com/method/adam) stochastic optimizer that introduces a term to rectify the variance of the adaptive learning rate. It seeks to tackle the bad convergence problem suffered by Adam. The authors argue that the root cause of this behaviour... |
Given the following machine learning model name: Intrinsically Motivated Goal Exploration Processes, provide a description of the model | Population-based intrinsically motivated goal exploration algorithms applied to real world robot learning of complex skills like tool use. |
Given the following machine learning model name: FixRes, provide a description of the model | **FixRes** is an image scaling strategy that seeks to optimize classifier performance. It is motivated by the observation that data augmentations induce a significant discrepancy between the size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification ... |
Given the following machine learning model name: Global-and-Local attention, provide a description of the model | Most attention mechanisms learn where to focus using only weak supervisory signals from class labels, which inspired Linsley et al. to investigate how explicit human supervision can affect the performance and interpretability of attention models. As a proof of concept, Linsley et al. proposed the global-and-local atten... |
Given the following machine learning model name: Factorization machines with cubic splines for numerical features, provide a description of the model | Using cubic splines to improve factorization machine accuracy with numerical features |
Given the following machine learning model name: Deformable DETR, provide a description of the model | **Deformable DETR** is an object detection method that aims mitigates the slow convergence and high complexity issues of [DETR](https://www.paperswithcode.com/method/detr). It combines the best of the sparse spatial sampling of [deformable convolution](https://paperswithcode.com/method/deformable-convolution), and the ... |
Given the following machine learning model name: VL-T5, provide a description of the model | VL-T5 is a unified framework that learns different tasks in a single architecture with the same language modeling objective, i.e., multimodal conditional text generation. The model learns to generate labels in text based on the visual and textual inputs. In contrast to other existing methods, the framework unifies task... |
Given the following machine learning model name: ScanSSD, provide a description of the model | **ScanSSD** is a single-shot Detector ([SSD](https://paperswithcode.com/method/ssd)) for locating math formulas offset from text and embedded in textlines. It uses only visual features for detection: no formatting or typesetting information such as layout, font, or character labels are employed. Given a 600 dpi documen... |
Given the following machine learning model name: Deep Q-Network, provide a description of the model | A **DQN**, or Deep Q-Network, approximates a state-value function in a [Q-Learning](https://paperswithcode.com/method/q-learning) framework with a neural network. In the Atari Games case, they take in several frames of the game as an input and output state values for each action as an output.
It is usually used in ... |
Given the following machine learning model name: Supporting Clustering with Contrastive Learning, provide a description of the model | **SCCL**, or **Supporting Clustering with Contrastive Learning**, is a framework to leverage contrastive learning to promote better separation in unsupervised clustering. It combines the top-down clustering with the bottom-up instance-wise contrastive learning to achieve better inter-cluster distance and intra-cluster ... |
Given the following machine learning model name: ClusterFit, provide a description of the model | **ClusterFit** is a self-supervision approach for learning image representations. Given a dataset, we (a) cluster its features extracted from a pre-trained network using k-means and (b) re-train a new network from scratch on this dataset using cluster assignments as pseudo-labels. |
Given the following machine learning model name: Residual GRU, provide a description of the model | A **Residual GRU** is a [gated recurrent unit (GRU)](https://paperswithcode.com/method/gru) that incorporates the idea of residual connections from [ResNets](https://paperswithcode.com/method/resnet). |
Given the following machine learning model name: Normalized Temperature-scaled Cross Entropy Loss, provide a description of the model | **NT-Xent**, or **Normalized Temperature-scaled Cross Entropy Loss**, is a loss function. Let $\text{sim}\left(\mathbf{u}, \mathbf{v}\right) = \mathbf{u}^{T}\mathbf{v}/||\mathbf{u}|| ||\mathbf{v}||$ denote the cosine similarity between two vectors $\mathbf{u}$ and $\mathbf{v}$. Then the loss function for a positive pai... |
Given the following machine learning model name: Neural Attention Fields, provide a description of the model | **NEAT**, or **Neural Attention Fields**, is a feature representation for end-to-end imitation learning models. NEAT is a continuous function which maps locations in Bird's Eye View (BEV) scene coordinates to waypoints and semantics, using intermediate attention maps to iteratively compress high-dimensional 2D image fe... |
Given the following machine learning model name: Modulated Residual Network, provide a description of the model | **MODERN**, or **Modulated Residual Network**, is an architecture for [visual question answering](https://paperswithcode.com/task/visual-question-answering) (VQA). It employs [conditional batch normalization](https://paperswithcode.com/method/conditional-batch-normalization) to allow a linguistic embedding from an [LST... |
Given the following machine learning model name: MelGAN, provide a description of the model | **MelGAN** is a non-autoregressive feed-forward convolutional architecture to perform audio waveform generation in a [GAN](https://paperswithcode.com/method/gan) setup. The architecture is a fully convolutional feed-forward network with mel-spectrogram $s$ as input and raw waveform $x$ as output. Since the mel-spectrog... |
Given the following machine learning model name: Self training multi target domain adaptive RetinaNet, provide a description of the model | |
Given the following machine learning model name: Mixture of Logistic Distributions, provide a description of the model | **Mixture of Logistic Distributions (MoL)** is a type of output function, and an alternative to a [softmax](https://paperswithcode.com/method/softmax) layer. Discretized logistic mixture likelihood is used in [PixelCNN](https://paperswithcode.com/method/pixelcnn)++ and [WaveNet](https://paperswithcode.com/method/wavene... |
Given the following machine learning model name: Procrustes, provide a description of the model | Procrustes |
Given the following machine learning model name: PSPNet, provide a description of the model | **PSPNet**, or **Pyramid Scene Parsing Network**, is a semantic segmentation model that utilises a pyramid parsing module that exploits global context information by different-region based context aggregation. The local and global clues together make the final prediction more reliable. We also propose an optimization
... |
Given the following machine learning model name: Local Augmentation, provide a description of the model | **Local Augmentation for Graph Neural Networks**, or **LA-GNN**, is a data augmentation technique that enhances node features by its local subgraph structures. Specifically, it learns the conditional distribution of the connected neighbors’ representations given the representation of the central node, which has an anal... |
Given the following machine learning model name: DeepMask, provide a description of the model | **DeepMask** is an object proposal algorithm based on a convolutional neural network. Given an input image patch, DeepMask generates a class-agnostic mask and an associated score which estimates the likelihood of the patch fully containing a centered object (without any notion of an object category). The core of the mo... |
Given the following machine learning model name: Soft Pooling, provide a description of the model | SoftPool: a fast and efficient method that sums exponentially weighted activations. Compared to a range of other pooling methods, SoftPool retains more information in the downsampled activation maps. More refined downsampling leads to better classification accuracy. |
Given the following machine learning model name: Attentive Walk-Aggregating Graph Neural Network, provide a description of the model | We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and end-to-end supervised GNN model, called AWARE (Attentive Walk-Aggregating GRaph Neural NEtwork), for graph-level prediction. AWARE aggregates... |
Given the following machine learning model name: Factor Graph Attention, provide a description of the model | A general multimodal attention unit for any number of modalities. Graphical models inspire it, i.e., it infers several attention beliefs via aggregated interaction messages. |
Given the following machine learning model name: Population Based Augmentation, provide a description of the model | **Population Based Augmentation**, or **PBA**, is a data augmentation strategy (PBA), which generates nonstationary augmentation policy schedules instead of a fixed augmentation policy. In PBA we consider the augmentation policy search problem as a special case of hyperparameter schedule learning. It leverages [Populat... |
Given the following machine learning model name: ResNet-RS, provide a description of the model | **ResNet-RS** is a family of [ResNet](https://paperswithcode.com/method/resnet) architectures that are 1.7x faster than [EfficientNets](https://paperswithcode.com/method/efficientnet) on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regime... |
Given the following machine learning model name: AlexNet, provide a description of the model | **AlexNet** is a classic convolutional neural network architecture. It consists of convolutions, [max pooling](https://paperswithcode.com/method/max-pooling) and dense layers as the basic building blocks. Grouped convolutions are used in order to fit the model across two GPUs. |
Given the following machine learning model name: Inverse Square Root Schedule, provide a description of the model | **Inverse Square Root** is a learning rate schedule 1 / $\sqrt{\max\left(n, k\right)}$ where
$n$ is the current training iteration and $k$ is the number of warm-up steps. This sets a constant learning rate for the first $k$ steps, then exponentially decays the learning rate until pre-training is over. |
Given the following machine learning model name: Variational Autoencoder, provide a description of the model | A **Variational Autoencoder** is a type of likelihood-based generative model. It consists of an encoder, that takes in data $x$ as input and transforms this into a latent representation $z$, and a decoder, that takes a latent representation $z$ and returns a reconstruction $\hat{x}$. Inference is performed via variati... |
Given the following machine learning model name: Temporal Activation Regularization, provide a description of the model | **Temporal Activation Regularization (TAR)** is a type of slowness regularization for [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks) that penalizes differences between states that have been explored in the past. Formally we minimize:
$$\beta{L\_{2}}\left(h\_{t} - h\_{t+1}\right)$$
w... |
Given the following machine learning model name: Attribute2Font, provide a description of the model | **Attribute2Font** is a model that automatically creates fonts by synthesizing visually pleasing glyph images according to user-specified attributes and their corresponding values. Specifically, Attribute2Font is trained to perform font style transfer between any two fonts conditioned on their attribute values. After t... |
Given the following machine learning model name: RelDiff, provide a description of the model | RelDiff generates entity-relation-entity embeddings in a single embedding space. RelDiff adopts two fundamental vector algebraic operators to transform entity and relation embeddings from knowledge graphs into entity-relation-entity embeddings. In particular, RelDiff can encode finer-grained information about the relat... |
Given the following machine learning model name: End-to-end Adaptive Distributed Training, provide a description of the model | Distributed training has become a pervasive and effective approach for training a large neural network
(NN) model with processing massive data. However, it is very challenging to satisfy requirements
from various NN models, diverse computing resources, and their dynamic changes during a training
job. In this study, ... |
Given the following machine learning model name: LLaMA, provide a description of the model | **LLaMA** is a collection of foundation language models ranging from 7B to 65B parameters. It is based on the transformer architecture with various improvements that were subsequently proposed. The main difference with the original architecture are listed below.
- RMSNorm normalizing function is used to improve the ... |
Given the following machine learning model name: Models Genesis, provide a description of the model | **Models Genesis**, or **Generic Autodidactic Models**, is a self-supervised approach for learning 3D image representations. The objective of Models Genesis is to learn a common image representation that is transferable and generalizable across diseases, organs, and modalities. It consists of an encoder-decoder archit... |
Given the following machine learning model name: HaloNet, provide a description of the model | A **HaloNet** is a self-attention based model for efficient image classification. It relies on a local self-attention architecture that efficiently maps to existing hardware with haloing. The formulation breaks translational equivariance, but the authors observe that it improves throughput and accuracies over the cent... |
Given the following machine learning model name: ConvMLP, provide a description of the model | **ConvMLP** is a hierarchical convolutional MLP for visual recognition, which consists of a stage-wise, co-design of [convolution](https://paperswithcode.com/method/convolution) layers, and MLPs. The Conv Stage consists of $C$ convolutional blocks with $1\times 1$ and $3\times 3$ kernel sizes. It is repeated $M$ times ... |
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