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Optimal Data Augmentation Strategy Search

A key challenge of object detection in practice is that there is only a limited number of images for training. Data augmentation techniques such as cropping, translation, and horizontal flipping are commonly used in deep learning for computer vision tasks. Moreover, data augmentation also acts as a regularizer to combat overfitting. However, less attention has been directed to discovering data...

Learning Space Partitions for Path Planning

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Dimension-free Statistical and Computational Guarantee for Optimal Transport

Optimal transport (OT) distances are increasingly used as loss functions for statistical inference, notably in the learning of generative models or supervised learning. For OT ideas to continue to bear fruit in ML, it will be necessary to tackle longstanding challenges, from both statistical and computational points of view: the computation is...

Hardware Software Co-Design for NLP and Recommendation Systems

This project investigates the co-design of Deep Neural Nets and their hardware support in Neural Net Accelerators.

Project Updates

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X-Ray for Lateral Access Mechanical Search

Efficiently finding an occluded object with lateral access arises in many contexts such as warehouses, retail, healthcare, shipping, and homes. We introduce LAX-RAY (Lateral Access maXimal Reduction of occupancY support Area), a system to automate...

NumS: NumPy API-Compatible Framework backed by Ray

Runtime improvements to nd-array and tensor operations increasingly rely on parallelism, and the scientific computing community has a growing need to train on more data. Systems and machine learning research has focused mostly on scaling machine learning workloads by designing scalable solutions for specific machine learning problems, such as data...

Animating Animals from Video

Note: we have changed our project to "lbsNeRF: Animatable Volumetric Avatars from Video"

Neural radiance fields (NeRF) have emerged as a promising representation for encoding geometry and appearance of static scenes and objects. However, extending these representations to capture the non-rigid deformations common in categories such as humans and animals remains an open challenge. Towards this goal, we propose lbsNeRF, a framework that learns an actor-specific neural avatar from multi-view videos with associated skeleton motions, and subsequently allows rendering under...

Safe Robotic Learning Via Reachability Theory

Neural network (NN) controllers trained through sampling of state-action trajectories, such as in reinforcement learning (RL), are becoming increasingly popular in the robotics community. For robotic tasks where safety is required, these methods can be problematic in two ways: first, they do not generally provide safety guarantees during training and, second, the...

Offline Recovery RL: Offline Reinforcement Learning with Safe Online Adaptation

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain...

Safe and Sound: Learning Locomotion Skills Across Robot Morphology

Learning locomotion policies on real robots can be risky. Free robot exploration for policy learning in the real world is necessary but dangerous as it may cause catastrophes for the robots, especially for large, heavy and complicated robots. In this work, we aim at learning policies for robotic locomotion tasks for risky robots with the goal of minimizing the interaction of these risky robots with the environments. Though learning on large, heavy and complicated robots tends to be risky, learning policies on smaller, lighter and simpler robots has much lower cost and risk. We...