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Found 14 papers, 2 papers with code
ProteinZero: Self-Improving Protein Generation via Online Reinforcement Learning
Protein generative models have shown remarkable promise in protein design but still face limitations in success rate, due to the scarcity of high-quality protein datasets for supervised pretraining.
$α$-Flow: A Unified Framework for Continuous-State Discrete Flow Matching Models
Recent efforts have extended the flow-matching framework to discrete generative modeling.
Dynamic Neural Dowker Network: Approximating Persistent Homology in Dynamic Directed Graphs
This paper introduces the Dynamic Neural Dowker Network (DNDN), a novel framework specifically designed to approximate the results of dynamic Dowker filtration, aiming to capture the high-order topological features of dynamic directed graphs.
PRANCE: Joint Token-Optimization and Structural Channel-Pruning for Adaptive ViT Inference
We introduce PRANCE, a Vision Transformer compression framework that jointly optimizes the activated channels and reduces tokens, based on the characteristics of inputs.
Proximity Matters: Local Proximity Preserved Balancing for Treatment Effect Estimation
Heterogeneous treatment effect (HTE) estimation from observational data poses significant challenges due to treatment selection bias.
ConvFormer: Revisiting Transformer for Sequential User Modeling
Sequential user modeling, a critical task in personalized recommender systems, focuses on predicting the next item a user would prefer, requiring a deep understanding of user behavior sequences.
Learnable Behavior Control: Breaking Atari Human World Records via Sample-Efficient Behavior Selection
The exploration problem is one of the main challenges in deep reinforcement learning (RL).
Ranked #1 on
Atari Games
on Atari-57
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications
As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues.
Generalized Data Distribution Iteration
Then, we cast these two problems into the training data distribution optimization problem, namely to obtain desired training data within limited interactions, and address them concurrently via i) explicit modeling and control of the capacity and diversity of behavior policy and ii) more fine-grained and adaptive control of selective/sampling distribution of the behavior policy using a monotonic data distribution optimization.
Ranked #1 on
Atari Games
on atari game
A Review for Deep Reinforcement Learning in Atari:Benchmarks, Challenges, and Solutions
From Deep Q-Networks (DQN) to Agent57, RL agents seem to achieve superhuman performance in ALE.
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning
Deep Q Network (DQN) firstly kicked the door of deep reinforcement learning (DRL) via combining deep learning (DL) with reinforcement learning (RL), which has noticed that the distribution of the acquired data would change during the training process.
Ranked #1 on
Atari Games
on Atari 2600 Freeway
An Entropy Regularization Free Mechanism for Policy-based Reinforcement Learning
We find valued-based reinforcement learning methods with {\epsilon}-greedy mechanism are capable of enjoying three characteristics, Closed-form Diversity, Objective-invariant Exploration and Adaptive Trade-off, which help value-based methods avoid the policy collapse problem.
CASA: Bridging the Gap between Policy Improvement and Policy Evaluation with Conflict Averse Policy Iteration
We study the problem of model-free reinforcement learning, which is often solved following the principle of Generalized Policy Iteration (GPI).
Critic PI2: Master Continuous Planning via Policy Improvement with Path Integrals and Deep Actor-Critic Reinforcement Learning
Extensive experiments demonstrate that Critic PI2 achieved a new state of the art in a range of challenging continuous domains.
