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Found 59 papers, 35 papers with code
Advances of Deep Learning in Protein Science: A Comprehensive Survey
Protein representation learning plays a crucial role in understanding the structure and function of proteins, which are essential biomolecules involved in various biological processes.
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks
In this paper, we identify two important collaborative processes for this topic: (1) select: how to select an optimal task combination from a given task pool based on their compatibility, and (2) weigh: how to weigh the selected tasks based on their importance.
Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge
Accurate prediction of protein-ligand binding structures, a task known as molecular docking is crucial for drug design but remains challenging.
Switch EMA: A Free Lunch for Better Flatness and Sharpness
Exponential Moving Average (EMA) is a widely used weight averaging (WA) regularization to learn flat optima for better generalizations without extra cost in deep neural network (DNN) optimization.
PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction
Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery.
A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer
Despite recent GNN and Graphformer efforts encoding graphs as Euclidean vectors, recovering original graph from the vectors remains a challenge.
FoldToken: Learning Protein Language via Vector Quantization and Beyond
Is there a foreign language describing protein sequences and structures simultaneously?
MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning
The scarcity of annotated data has sparked significant interest in unsupervised pre-training methods that leverage medical reports as auxiliary signals for medical visual representation learning.
DCS-Net: Pioneering Leakage-Free Point Cloud Pretraining Framework with Global Insights
Experimental results demonstrate that our method enhances the expressive capacity of existing point cloud models and effectively addresses the issue of information leakage.
Deep Manifold Transformation for Protein Representation Learning
Protein representation learning is critical in various tasks in biology, such as drug design and protein structure or function prediction, which has primarily benefited from protein language models and graph neural networks.
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.
Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
As the deep learning revolution marches on, self-supervised learning has garnered increasing attention in recent years thanks to its remarkable representation learning ability and the low dependence on labeled data.
MMDesign: Multi-Modality Transfer Learning for Generative Protein Design
Protein design involves generating protein sequences based on their corresponding protein backbones.
Efficiently Predicting Protein Stability Changes Upon Single-point Mutation with Large Language Models
Predicting protein stability changes induced by single-point mutations has been a persistent challenge over the years, attracting immense interest from numerous researchers.
Boosting the Power of Small Multimodal Reasoning Models to Match Larger Models with Self-Consistency Training
Remarkably, we show that even smaller base models, when equipped with our proposed approach, can achieve results comparable to those of larger models, illustrating the potential of our approach in harnessing the power of rationales for improved multimodal reasoning.
Segment Anything in Defect Detection
Defect detection plays a crucial role in infrared non-destructive testing systems, offering non-contact, safe, and efficient inspection capabilities.
General Point Model with Autoencoding and Autoregressive
This model is versatile, allowing fine-tuning for downstream point cloud representation tasks, as well as unconditional and conditional generation tasks.
Revisiting the Temporal Modeling in Spatio-Temporal Predictive Learning under A Unified View
In this paper, we re-examine the two dominant temporal modeling approaches within the realm of spatio-temporal predictive learning, offering a unified perspective.
SemiReward: A General Reward Model for Semi-supervised Learning
The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias.
Dropout Attacks
DROPOUTATTACK attacks the dropout operator by manipulating the selection of neurons to drop instead of selecting them uniformly at random.
CONVERT:Contrastive Graph Clustering with Reliable Augmentation
To address these problems, we propose a novel CONtrastiVe Graph ClustEring network with Reliable AugmenTation (CONVERT).
Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset and Comprehensive Framework
Multimodal reasoning is a critical component in the pursuit of artificial intelligence systems that exhibit human-like intelligence, especially when tackling complex tasks.
OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning
Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner.
Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement
After witnessing the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further.
Ranked #1 on
Word Sense Disambiguation
on TS50
MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing
Is there a unified framework for graph-based retrosynthesis prediction?
Cross-Gate MLP with Protein Complex Invariant Embedding is A One-Shot Antibody Designer
In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner.
Lightweight Contrastive Protein Structure-Sequence Transformation
In this work, we introduce a novel unsupervised protein structure representation pretraining with a robust protein language model.
CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment
In this work, we propose a novel contrastive visual-textual transformation for SLR, CVT-SLR, to fully explore the pretrained knowledge of both the visual and language modalities.
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
Is there a unified model for generating molecules considering different conditions, such as binding pockets and chemical properties?
RDesign: Hierarchical Data-efficient Representation Learning for Tertiary Structure-based RNA Design
We crafted a large, well-curated benchmark dataset and designed a comprehensive structural modeling approach to represent the complex RNA tertiary structure.
DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraints
Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling?
RFold: RNA Secondary Structure Prediction with Decoupled Optimization
The secondary structure of ribonucleic acid (RNA) is more stable and accessible in the cell than its tertiary structure, making it essential for functional prediction.
Protein Language Models and Structure Prediction: Connection and Progression
The prediction of protein structures from sequences is an important task for function prediction, drug design, and related biological processes understanding.
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
In an era of countless content offerings, recommender systems alleviate information overload by providing users with personalized content suggestions.
SimVP: Towards Simple yet Powerful Spatiotemporal Predictive Learning
Without introducing any extra tricks and strategies, SimVP can achieve superior performance on various benchmark datasets.
Ranked #1 on
Video Prediction
on Moving MNIST
MogaNet: Multi-order Gated Aggregation Network
Notably, MogaNet hits 80. 0\% and 87. 8\% accuracy with 5. 2M and 181M parameters on ImageNet-1K, outperforming ParC-Net and ConvNeXt-L, while saving 59\% FLOPs and 17M parameters, respectively.
Ranked #1 on
Pose Estimation
on COCO val2017
Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation
Therefore, we propose to introduce additional word-level semantic knowledge of sign language linguistics to assist in improving current end-to-end neural SLT models.
PiFold: Toward effective and efficient protein inverse folding
How can we design protein sequences folding into the desired structures effectively and efficiently?
OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification
Data mixing, or mixup, is a data-dependent augmentation technique that has greatly enhanced the generalizability of modern deep neural networks.
A Survey on Generative Diffusion Model
Deep generative models are a prominent approach for data generation, and have been used to produce high quality samples in various domains.
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning
Spatiotemporal predictive learning aims to generate future frames by learning from historical frames.
Ranked #12 on
Video Prediction
on Moving MNIST
CoSP: Co-supervised pretraining of pocket and ligand
Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space?
SimVP: Simpler yet Better Video Prediction
From CNN, RNN, to ViT, we have witnessed remarkable advancements in video prediction, incorporating auxiliary inputs, elaborate neural architectures, and sophisticated training strategies.
Ranked #2 on
Video Prediction
on Human3.6M
Hyperspherical Consistency Regularization
Though it benefits from taking advantage of both feature-dependent information from self-supervised learning and label-dependent information from supervised learning, this scheme remains suffering from bias of the classifier.
Generative De Novo Protein Design with Global Context
Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules.
Harnessing Hard Mixed Samples with Decoupled Regularizer
However, we found that the extra optimizing step may be redundant because label-mismatched mixed samples are informative hard mixed samples for deep models to localize discriminative features.
I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization
In this paper we propose a novel hardware accelerator for GCN inference, called I-GCN, that significantly improves data locality and reduces unnecessary computation.
SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
Experimental results show that SemiRetro significantly outperforms both existing TB and TF methods.
Target-aware Molecular Graph Generation
Building on the recent advantages of flow-based molecular generation models, we propose SiamFlow, which forces the flow to fit the distribution of target sequence embeddings in latent space.
AlphaDesign: A graph protein design method and benchmark on AlphaFoldDB
While DeepMind has tentatively solved protein folding, its inverse problem -- protein design which predicts protein sequences from their 3D structures -- still faces significant challenges.
Prediction of GPU Failures Under Deep Learning Workloads
To improve the precision and stability of predictions, we propose several techniques, including parallel and cascade model-ensemble mechanisms and a sliding training method.
An Empirical Study: Extensive Deep Temporal Point Process
In this paper, we first review recent research emphasis and difficulties in modeling asynchronous event sequences with deep temporal point process, which can be concluded into four fields: encoding of history sequence, formulation of conditional intensity function, relational discovery of events and learning approaches for optimization.
Git: Clustering Based on Graph of Intensity Topology
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm.
Ranked #1 on
Clustering Algorithms Evaluation
on Fashion-MNIST
Serving DNN Models with Multi-Instance GPUs: A Case of the Reconfigurable Machine Scheduling Problem
With MIG, A100 can be the most cost-efficient GPU ever for serving Deep Neural Networks (DNNs).
Co-learning: Learning from Noisy Labels with Self-supervision
Noisy labels, resulting from mistakes in manual labeling or webly data collecting for supervised learning, can cause neural networks to overfit the misleading information and degrade the generalization performance.
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction
Recent years have witnessed great success in handling node classification tasks with Graph Neural Networks (GNNs).
Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive
In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data.
Recursive Exponential Weighting for Online Non-convex Optimization
In this paper, we investigate the online non-convex optimization problem which generalizes the classic {online convex optimization problem by relaxing the convexity assumption on the cost function.
