Technology
New Bounds for Hyperparameter Tuning of Regression Problems Across Instances
In this work, we investigate an alternative setting for tuning regularization parameters, namely data-driven algorithm design, following the previous line of work by Balcan et al. [
FlexCap: Describe Anything in Images in Controllable Detail
We demonstrate FlexCap's effectiveness in several applications: first, it achieves strong performance in dense captioning tasks on the Visual Genome dataset. Second, we show how FlexCap's localized
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.
Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask?
PaI methods manage to find trainable subnetworks that outperform random pruning, their performance in terms of both accuracy and computational reduction is far from satisfactory compared to post-training pruning and the understanding of PaI is missing.
Context Shift Reduction for Offline Meta-Reinforcement Learning Y unkai Gao
Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks.
FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models Supplementary Materials 1 Dataset 1.1 Links and Preservation
The croissant metadata record is available at croissant. We chose GitHub and Google Drive respectively to store our code and dataset. Both are widely recognized as reliable data storage platforms, ensuring long-term preservation. We highly recommend downloading the raw data directly and following the provided instructions to simplify the data processing steps. Our dataset is structured as follows: the local directory contains client-specific data for local training, while all clients aggregates data from all clients for federated learning.
