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Found 11 papers, 8 papers with code
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance.
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition
Speech emotion recognition (SER) is a pivotal technology for human-computer interaction systems.
Deep Reinforcement Learning for Quantitative Trading
Incorporating deep reinforcement learning (DRL) with imitative learning methodologies, we bolster the proficiency of our model.
Spanning Training Progress: Temporal Dual-Depth Scoring (TDDS) for Enhanced Dataset Pruning
Dataset pruning aims to construct a coreset capable of achieving performance comparable to the original, full dataset.
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
To mitigate the adverse impact of this accumulated trajectory error, we propose a novel approach that encourages the optimization algorithm to seek a flat trajectory.
Sharpness-Aware Training for Free
Intuitively, SAF achieves this by avoiding sudden drops in the loss in the sharp local minima throughout the trajectory of the updates of the weights.
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks
Recently, the relation between the sharpness of the loss landscape and the generalization error has been established by Foret et al. (2020), in which the Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the generalization.
A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19
We also found that the epidemic has a significant negative impact on the return of the energy sector, and finally we provided our suggestions to investors.
RAIN: A Simple Approach for Robust and Accurate Image Classification Networks
However, similar to existing preprocessing-based methods, the randomized process will degrade the prediction accuracy.
On Robustness of Neural Ordinary Differential Equations
We then provide an insightful understanding of this phenomenon by exploiting a certain desirable property of the flow of a continuous-time ODE, namely that integral curves are non-intersecting.
Query-efficient Meta Attack to Deep Neural Networks
Black-box attack methods aim to infer suitable attack patterns to targeted DNN models by only using output feedback of the models and the corresponding input queries.
