Search Results for author:
Found 10 papers, 6 papers with code
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable.
Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images
Experimental results show that our model can learn color and affine transformations that are more helpful to train an image classifier than predefined DA transformations, which are also more expensive as they need to be selected before the training by grid search on a validation set.
Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning
The main challenge is that the agent must not forget previous tasks and also adapt to novel tasks in the stream.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
Slanted Stixels: Representing San Francisco's Steepest Streets
In this work we present a novel compact scene representation based on Stixels that infers geometric and semantic information.
A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images
Colorectal cancer (CRC) is the third cause of cancer death worldwide.
GPU-based Pedestrian Detection for Autonomous Driving
We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform.
Embedded real-time stereo estimation via Semi-Global Matching on the GPU
Dense, robust and real-time computation of depth information from stereo-camera systems is a computationally demanding requirement for robotics, advanced driver assistance systems (ADAS) and autonomous vehicles.
GPU-accelerated real-time stixel computation
The Stixel World is a medium-level, compact representation of road scenes that abstracts millions of disparity pixels into hundreds or thousands of stixels.
Spatiotemporal Stacked Sequential Learning for Pedestrian Detection
In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood.
