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Found 17 papers, 7 papers with code
The Latent Road to Atoms: Backmapping Coarse-grained Protein Structures with Latent Diffusion
Coarse-grained(CG) molecular dynamics simulations offer computational efficiency for exploring protein conformational ensembles and thermodynamic properties.
Pre-training with Fractional Denoising to Enhance Molecular Property Prediction
Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design.
Compressing Lengthy Context With UltraGist
Compressing lengthy context is a critical but technically challenging problem.
Extending Llama-3's Context Ten-Fold Overnight
We extend the context length of Llama-3-8B-Instruct from 8K to 80K via QLoRA fine-tuning.
SGNet: Folding Symmetrical Protein Complex with Deep Learning
Deep learning has made significant progress in protein structure prediction, advancing the development of computational biology.
Do Efficient Transformers Really Save Computation?
Our results show that while these models are expressive enough to solve general DP tasks, contrary to expectations, they require a model size that scales with the problem size.
Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
Specifically, we identify a fundamental expressivity measure termed homomorphism expressivity, which quantifies the ability of GNN models to count graphs under homomorphism.
Long Context Compression with Activation Beacon
In this paper, we propose Activation Beacon, a plug-in module for transformer-based LLMs that targets effective, efficient, and flexible compression of long contexts.
Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning
The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling.
Particle Based Stochastic Policy Optimization
Stochastic polic have been widely applied for their good property in exploration and uncertainty quantification.
Ranked #1 on
MuJoCo Games
on Ant-v3
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances.
Discriminator Contrastive Divergence: Semi-Amortized Generative Modeling by Exploring Energy of the Discriminator
Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data.
Ranked #13 on
Image Generation
on STL-10
Suphx: Mastering Mahjong with Deep Reinforcement Learning
Artificial Intelligence (AI) has achieved great success in many domains, and game AI is widely regarded as its beachhead since the dawn of AI.
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
Based on that, We proposed an end-to-end algorithm to learn exploration policy by meta-learning.
Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
In reinforcement learning, Return, which is the weighted accumulated future rewards, and Value, which is the expected return, serve as the objective that guides the learning of the policy.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
We prove that, since the data instances with larger gradients play a more important role in the computation of information gain, GOSS can obtain quite accurate estimation of the information gain with a much smaller data size.
A Communication-Efficient Parallel Algorithm for Decision Tree
After partitioning the training data onto a number of (e. g., $M$) machines, this algorithm performs both local voting and global voting in each iteration.
