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Found 9 papers, 8 papers with code
Compressed Context Memory For Online Language Model Interaction
This paper presents a context key/value compression method for Transformer language models in online scenarios, where the context continually expands.
Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data
To this end, we present scalable and effective algorithms for detecting label errors and outlier data based on the relational graph structure of data.
Dataset Condensation via Efficient Synthetic-Data Parameterization
The great success of machine learning with massive amounts of data comes at a price of huge computation costs and storage for training and tuning.
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem.
Ranked #1 on
Gym halfcheetah-random
on D4RL
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity
While deep neural networks show great performance on fitting to the training distribution, improving the networks' generalization performance to the test distribution and robustness to the sensitivity to input perturbations still remain as a challenge.
Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
While deep neural networks achieve great performance on fitting the training distribution, the learned networks are prone to overfitting and are susceptible to adversarial attacks.
Ranked #2 on
Image Classification
on Tiny-ImageNet
Spherical Principal Curves
In this study, we propose a new approach to construct principal curves on a sphere by a projection of the data onto a continuous curve.
Phase-aware Speech Enhancement with Deep Complex U-Net
Most deep learning-based models for speech enhancement have mainly focused on estimating the magnitude of spectrogram while reusing the phase from noisy speech for reconstruction.
Multi-Domain Processing via Hybrid Denoising Networks for Speech Enhancement
We present a hybrid framework that leverages the trade-off between temporal and frequency precision in audio representations to improve the performance of speech enhancement task.
Audio and Speech Processing Sound
