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Found 7 papers, 2 papers with code
Perceiver IO: A General Architecture for Structured Inputs & Outputs
The recently-proposed Perceiver model obtains good results on several domains (images, audio, multimodal, point clouds) while scaling linearly in compute and memory with the input size.
Ranked #1 on
Optical Flow Estimation
on Sintel-final
Efficient Visual Pretraining with Contrastive Detection
Self-supervised pretraining has been shown to yield powerful representations for transfer learning.
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
Accurate, Low-Latency Visual Perception for Autonomous Racing:Challenges, Mechanisms, and Practical Solutions
Autonomous racing provides the opportunity to test safety-critical perception pipelines at their limit.
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
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
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
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.