Search Results for author: Skanda Koppula

Found 17 papers, 9 papers with code

Memory Consolidation Enables Long-Context Video Understanding

no code implementations8 Feb 2024 Ivana Balažević, Yuge Shi, Pinelopi Papalampidi, Rahma Chaabouni, Skanda Koppula, Olivier J. Hénaff

Most transformer-based video encoders are limited to short temporal contexts due to their quadratic complexity.

Video Understanding

BootsTAP: Bootstrapped Training for Tracking-Any-Point

2 code implementations1 Feb 2024 Carl Doersch, Yi Yang, Dilara Gokay, Pauline Luc, Skanda Koppula, Ankush Gupta, Joseph Heyward, Ross Goroshin, João Carreira, Andrew Zisserman

To endow models with greater understanding of physics and motion, it is useful to enable them to perceive how solid surfaces move and deform in real scenes.

Lossless Adaptation of Pretrained Vision Models For Robotic Manipulation

no code implementations13 Apr 2023 Mohit Sharma, Claudio Fantacci, Yuxiang Zhou, Skanda Koppula, Nicolas Heess, Jon Scholz, Yusuf Aytar

We demonstrate that appropriate placement of our parameter efficient adapters can significantly reduce the performance gap between frozen pretrained representations and full end-to-end fine-tuning without changes to the original representation and thus preserving original capabilities of the pretrained model.

HiP: Hierarchical Perceiver

2 code implementations22 Feb 2022 Joao Carreira, Skanda Koppula, Daniel Zoran, Adria Recasens, Catalin Ionescu, Olivier Henaff, Evan Shelhamer, Relja Arandjelovic, Matt Botvinick, Oriol Vinyals, Karen Simonyan, Andrew Zisserman, Andrew Jaegle

This however hinders them from scaling up to the inputs sizes required to process raw high-resolution images or video.

EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM

no code implementations12 Oct 2019 Skanda Koppula, Lois Orosa, Abdullah Giray Yağlıkçı, Roknoddin Azizi, Taha Shahroodi, Konstantinos Kanellopoulos, Onur Mutlu

Based on this observation, we propose EDEN, a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy.

Understanding Recurrent Neural State Using Memory Signatures

no code implementations11 Feb 2018 Skanda Koppula, Khe Chai Sim, Kean Chin

We demonstrate this method's usefulness in revealing information divergence in the bases of recurrent factorized kernels, visualizing the character-level differences between the memory of n-gram and recurrent language models, and extracting knowledge of history encoded in the layers of grapheme-based end-to-end ASR networks.

Learning a CNN-based End-to-End Controller for a Formula SAE Racecar

no code implementations12 Jul 2017 Skanda Koppula

We present a set of CNN-based end-to-end models for controls of a Formula SAE racecar, along with various benchmarking and visualization tools to understand model performance.


Cannot find the paper you are looking for? You can Submit a new open access paper.