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Found 32 papers, 20 papers with code
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.
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.
Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction
To study this problem, we identify a Protein 3D Graph Structure Learning Problem for Robust Protein Property Prediction (PGSL-RP3), collect benchmark datasets, and present a protein Structure embedding Alignment Optimization framework (SAO) to mitigate the problem of structure embedding bias between the predicted and experimental protein structures.
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.
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.
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.
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?
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.
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
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification
Despite their great academic success, Multi-Layer Perceptrons (MLPs) remain the primary workhorse for practical industrial applications.
PiFold: Toward effective and efficient protein inverse folding
How can we design protein sequences folding into the desired structures effectively and efficiently?
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.
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.
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.
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 MNIST
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.
Conditional Local Convolution for Spatio-temporal Meteorological Forecasting
We further propose the distance and orientation scaling terms to reduce the impacts of irregular spatial distribution.
Towards Robust Graph Neural Networks against Label Noise
A pseudo label is computed from the neighboring labels for each node in the training set using LP; meta learning is utilized to learn a proper aggregation of the original and pseudo label as the final label.
LookHops: light multi-order convolution and pooling for graph classification
Convolution and pooling are the key operations to learn hierarchical representation for graph classification, where more expressive $k$-order($k>1$) method requires more computation cost, limiting the further applications.
Clustering Based on Graph of Density Topology
GDT jointly considers the local and global structures of data samples: firstly forming local clusters based on a density growing process with a strategy for properly noise handling as well as cluster boundary detection; and then estimating a GDT from relationship between local clusters in terms of a connectivity measure, givingglobal topological graph.
