Latent diffusion models for image generation have crossed a quality threshold which enabled
them to achieve mass adoption. Recently, a series of works have made advancements …

Over the last decade, online lecture videos have become increasingly popular and have
experienced a meteoric rise during the pandemic. However, video-language research has …

Scaling semantic parsing models for task-oriented dialog systems to new languages is often
expensive and time-consuming due to the lack of available datasets. Available datasets …

We propose pre-finetuning, an additional large-scale learning stage between language
model pre-training and fine-tuning. Pre-finetuning is massively multi-task learning (around 50 …

Many NLP tasks require processing long contexts beyond the length limit of pretrained
models. In order to scale these models to longer text sequences, many efficient long-range …

… Ancient India. But the patient investigations of numerous scholars and archæologists have
opened up rich stores of material for the reconstruction of the ancient … history of Ancient India …

Large, high-capacity models trained on diverse datasets have shown remarkable successes
on efficiently tackling downstream applications. In domains from NLP to Computer Vision, …

Imitation Learning has empowered recent advances in learning robotic manipulation tasks
by addressing shortcomings of Reinforcement Learning such as exploration and reward …

Although widely adopted, existing approaches for fine-tuning pre-trained language models
have been shown to be unstable across hyper-parameter settings, motivating recent work on …

Reproducibility has been a significant challenge in deep reinforcement learning and robotics
research. Open-source frameworks and standardized benchmarks can serve an integral …