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Found 13 papers, 5 papers with code
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.
Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon
Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities.
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 #7 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.
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.
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.
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.
