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Found 5 papers, 3 papers with code
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query.
Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample importance weighting.
Introspective Experience Replay: Look Back When Surprised
In reinforcement learning (RL), experience replay-based sampling techniques play a crucial role in promoting convergence by eliminating spurious correlations.
The Effect of Diversity in Meta-Learning
Recent studies show that task distribution plays a vital role in the meta-learner's performance.
Boosting Exploration in Multi-Task Reinforcement Learning using Adversarial Networks
Our proposed adversarial training regime for Multi-Task Reinforcement Learning (MT-RL) addresses the limitations of conventional training methods in RL, especially in meta-RL environments where the agent faces new tasks.
