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Found 6 papers, 3 papers with code
On the Limits of Multi-modal Meta-Learning with Auxiliary Task Modulation Using Conditional Batch Normalization
While the classifier performs the main classification task, the auxiliary network learns to predict language representations from the same input, and the bridge network transforms high-level features of the auxiliary network into modulation parameters for layers of the few-shot classifier using conditional batch normalization.
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.
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.
The Effect of Diversity in Meta-Learning
Recent studies show that task distribution plays a vital role in the meta-learner's performance.
