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Found 7 papers, 5 papers with code
AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark
AuroraCap shows superior performance on various video and image captioning benchmarks, for example, obtaining a CIDEr of 88. 9 on Flickr30k, beating GPT-4V (55. 3) and Gemini-1. 5 Pro (82. 2).
DECORE: Deep Compression with Reinforcement Learning
For a larger dataset like ImageNet with just 30 epochs of training, it can compress the ResNet-50 architecture by 44. 7% and reduce FLOPs by 42. 3%, with just a 0. 69% drop on Top-5 accuracy of the uncompressed model.
Scaling MAP-Elites to Deep Neuroevolution
Quality-Diversity (QD) algorithms, and MAP-Elites (ME) in particular, have proven very useful for a broad range of applications including enabling real robots to recover quickly from joint damage, solving strongly deceptive maze tasks or evolving robot morphologies to discover new gaits.
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents
We lessen this friction, by (1) training several algorithms at scale and releasing trained models, (2) integrating with a previous Deep RL model release, and (3) releasing code that makes it easy for anyone to load, visualize, and analyze such models.
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.
Ranked #5 on
Multiple Object Tracking
on BDD100K test
Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep neural networks roughly as well as Q-learning and policy gradient methods on challenging deep reinforcement learning (RL) problems, but are much faster (e. g. hours vs. days) because they parallelize better.
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion.
