Plasticity, the ability of a neural network to quickly change its predictions in response to new
information, is essential for the adaptability and robustness of deep reinforcement learning …

Although very successfully used in conventional machine learning, convolution based
neural network architectures--believed to be inconsistent in function space--have been …

Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …

The limited priors required by neural networks make them the dominating choice to encode
and learn policies using reinforcement learning (RL). However, they are also black-boxes …

Due to their substantial sizes, large language models (LLMs) are typically deployed within a
single-backbone multi-tenant framework. In this setup, a single instance of an LLM …

Soft prompt tuning is a widely studied parameter-efficient fine-tuning method. However, it
has a clear drawback: many soft tokens must be inserted into the input sequences to …

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many
issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal …

State-of-the-art pretrained NLP models contain a hundred million to trillion parameters.
Adapters provide a parameter-efficient alternative for the full finetuning in which we can only …

Recent unsupervised multi-object detection models have shown impressive performance
improvements, largely attributed to novel architectural inductive biases. Unfortunately …

Natural intelligence processes experience as a continuous stream, sensing, acting, and
learning moment-by-moment in real time. Streaming learning, the modus operandi of classic …