Search Results for author:
Found 8 papers, 3 papers with code
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields
We study the effectiveness of training equivariant networks with DeNS on OC20, OC22 and MD17 datasets and demonstrate that DeNS can achieve new state-of-the-art results on OC20 and OC22 and significantly improve training efficiency on MD17.
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems.
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
Despite their widespread success in various domains, Transformer networks have yet to perform well across datasets in the domain of 3D atomistic graphs such as molecules even when 3D-related inductive biases like translational invariance and rotational equivariance are considered.
Graph Attention
Initial Structure to Relaxed Energy (IS2RE), Direct
On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis
Are end-to-end text-to-speech (TTS) models over-parametrized?
Searching for Efficient Multi-Stage Vision Transformers
This naturally raises the question of how the performance of ViT can be advanced with design techniques of CNN.
PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition
We investigate the existence of sparse subnetworks in pre-trained speech SSL models that achieve even better low-resource ASR results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
NetAdaptV2: Efficient Neural Architecture Search with Fast Super-Network Training and Architecture Optimization
Neural architecture search (NAS) typically consists of three main steps: training a super-network, training and evaluating sampled deep neural networks (DNNs), and training the discovered DNN.
3D Shape Reconstruction from a Single 2D Image via 2D-3D Self-Consistency
Aiming at inferring 3D shapes from 2D images, 3D shape reconstruction has drawn huge attention from researchers in computer vision and deep learning communities.
3D Reconstruction
3D Shape Reconstruction From A Single 2D Image
+1
