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Found 11 papers, 5 papers with code
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Our approach achieves state-of-the-art results in terms of Interquartile Mean (IQM) performance and Optimality Gap reduction on the Unsupervised Reinforcement Learning Benchmark for model-free methods, recording an 86% IQM and a 16% Optimality Gap.
TeacherLM: Teaching to Fish Rather Than Giving the Fish, Language Modeling Likewise
Based on TeacherLM-7. 1B, we augmented 58 NLP datasets and taught various student models with different parameters from OPT and BLOOM series in a multi-task setting.
AgentTuning: Enabling Generalized Agent Abilities for LLMs
Though many prompting methods have been proposed to complete particular agent tasks, there is lack of research focusing on improving the agent capabilities of LLMs themselves without compromising their general abilities.
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL.
EfficientTrain: Exploring Generalized Curriculum Learning for Training Visual Backbones
It is also effective for self-supervised learning (e. g., MAE).
Provable General Function Class Representation Learning in Multitask Bandits and MDPs
While multitask representation learning has become a popular approach in reinforcement learning (RL) to boost the sample efficiency, the theoretical understanding of why and how it works is still limited.
On the Integration of Self-Attention and Convolution
In this paper, we show that there exists a strong underlying relation between them, in the sense that the bulk of computations of these two paradigms are in fact done with the same operation.
On the Power of Multitask Representation Learning in Linear MDP
We first discover a \emph{Least-Activated-Feature-Abundance} (LAFA) criterion, denoted as $\kappa$, with which we prove that a straightforward least-square algorithm learns a policy which is $\tilde{O}(H^2\sqrt{\frac{\mathcal{C}(\Phi)^2 \kappa d}{NT}+\frac{\kappa d}{n}})$ sub-optimal.
A Deep Prediction Network for Understanding Advertiser Intent and Satisfaction
For e-commerce platforms such as Taobao and Amazon, advertisers play an important role in the entire digital ecosystem: their behaviors explicitly influence users' browsing and shopping experience; more importantly, advertiser's expenditure on advertising constitutes a primary source of platform revenue.
Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning
On one hand, considering the relevance between edge and orientation, two sub-networks are designed to share the occlusion cue.
Context-Constrained Accurate Contour Extraction for Occlusion Edge Detection
Occlusion edge detection requires both accurate locations and context constraints of the contour.
