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Found 21 papers, 14 papers with code
Fast Registration of Photorealistic Avatars for VR Facial Animation
In this work, we first show that the domain gap between the avatar and headset-camera images is one of the primary sources of difficulty, where a transformer-based architecture achieves high accuracy on domain-consistent data, but degrades when the domain-gap is re-introduced.
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations
We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction.
Path Independent Equilibrium Models Can Better Exploit Test-Time Computation
Designing networks capable of attaining better performance with an increased inference budget is important to facilitate generalization to harder problem instances.
Stability of Weighted Majority Voting under Estimated Weights
To formally analyse the uncertainty to the decision process, we introduce and analyse two important properties of such unbiased trust values: stability of correctness and stability of optimality.
Deep Equilibrium Optical Flow Estimation
Many recent state-of-the-art (SOTA) optical flow models use finite-step recurrent update operations to emulate traditional algorithms by encouraging iterative refinements toward a stable flow estimation.
Ranked #1 on
Optical Flow Estimation
on KITTI 2015 (train)
$(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations
Recent research in deep learning has investigated two very different forms of ''implicitness'': implicit representations model high-frequency data such as images or 3D shapes directly via a low-dimensional neural network (often using e. g., sinusoidal bases or nonlinearities); implicit layers, in contrast, refer to techniques where the forward pass of a network is computed via non-linear dynamical systems, such as fixed-point or differential equation solutions, with the backward pass computed via the implicit function theorem.
Joint inference and input optimization in equilibrium networks
Many tasks in deep learning involve optimizing over the \emph{inputs} to a network to minimize or maximize some objective; examples include optimization over latent spaces in a generative model to match a target image, or adversarially perturbing an input to worsen classifier performance.
Neural Deep Equilibrium Solvers
A deep equilibrium (DEQ) model abandons traditional depth by solving for the fixed point of a single nonlinear layer $f_\theta$.
Stabilizing Equilibrium Models by Jacobian Regularization
Deep equilibrium networks (DEQs) are a new class of models that eschews traditional depth in favor of finding the fixed point of a single nonlinear layer.
SHINE: SHaring the INverse Estimate from the forward pass for bi-level optimization and implicit models
In Deep Equilibrium Models (DEQs), the training is performed as a bi-level problem, and its computational complexity is partially driven by the iterative inversion of a huge Jacobian matrix.
A Note on Connecting Barlow Twins with Negative-Sample-Free Contrastive Learning
In this report, we relate the algorithmic design of Barlow Twins' method to the Hilbert-Schmidt Independence Criterion (HSIC), thus establishing it as a contrastive learning approach that is free of negative samples.
A community-powered search of machine learning strategy space to find NMR property prediction models
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions.
Ranked #1 on
NMR J-coupling
on QM9
Multiscale Deep Equilibrium Models
These simultaneously-learned multi-resolution features allow us to train a single model on a diverse set of tasks and loss functions, such as using a single MDEQ to perform both image classification and semantic segmentation.
Ranked #46 on
Semantic Segmentation
on Cityscapes val
Transformer Dissection: An Unified Understanding for Transformer's Attention via the Lens of Kernel
This new formulation gives us a better way to understand individual components of the Transformer{'}s attention, such as the better way to integrate the positional embedding.
Deep Equilibrium Models
We present a new approach to modeling sequential data: the deep equilibrium model (DEQ).
Ranked #29 on
Language Modelling
on Penn Treebank (Word Level)
Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kernel
This new formulation gives us a better way to understand individual components of the Transformer's attention, such as the better way to integrate the positional embedding.
Multimodal Transformer for Unaligned Multimodal Language Sequences
Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors.
Ranked #5 on
Multimodal Sentiment Analysis
on MOSI
Trellis Networks for Sequence Modeling
On the other hand, we show that truncated recurrent networks are equivalent to trellis networks with special sparsity structure in their weight matrices.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
Our results indicate that a simple convolutional architecture outperforms canonical recurrent networks such as LSTMs across a diverse range of tasks and datasets, while demonstrating longer effective memory.
Ranked #4 on
Music Modeling
on Nottingham
Convolutional Sequence Modeling Revisited
This paper revisits the problem of sequence modeling using convolutional architectures.
Ranked #84 on
Language Modelling
on WikiText-103
