7 accepted papers
Since the release of ChatGPT, large language models (LLMs) have demonstrated remarkable capabilities across various domains. A key challenge in developing these general capabilities is efficiently sourcing diverse, high-quality data. This becomes especially critical in reasoning-related tasks with s…
Mixture-of-Experts (MoEs) achieve scalability by dynamically activating subsets of their components. Yet, understanding how expertise emerges through joint training of gating mechanisms and experts remains incomplete, especially in scenarios without clear task partitions. Motivated by inference cost…
Prompt instruction tuning is a popular approach to better adjust pretrained LLMs for specific downstream tasks. How to extend this approach to simultaneously handle multiple tasks and data distributions is an interesting question. We propose Mixture of Prompts (MoPs) with smart gating functionality.…
Asynchronous learning protocols have regained attention lately, especially in the Federated Learning (FL) setup, where slower clients can severely impede the learning process. Herein, we propose AsyncDrop, a novel asynchronous FL framework that utilizes dropout regularization to handle device hetero…
Recent work on the Lottery Ticket Hypothesis (LTH) shows that there exist “winning tickets” in large neural networks. These tickets represent “sparse” versions of the full model that can be trained independently to achieve comparable accuracy with respect to the full model. However, finding the winn…
We propose ResIST, a novel distributed training protocol for Residual Networks (ResNets). ResIST randomly decomposes a global ResNet into several shallow sub-ResNets that are trained independently in a distributed manner for several local iterations, before having their updates synchronized and aggr…
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representati…