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Found 10 papers, 7 papers with code
Faces: AI Blitz XIII Solutions
AI Blitz XIII Faces challenge hosted on www. aicrowd. com platform consisted of five problems: Sentiment Classification, Age Prediction, Mask Prediction, Face Recognition, and Face De-Blurring.
Traffic4cast at NeurIPS 2021 -- Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins.
A Graph-based U-Net Model for Predicting Traffic in unseen Cities
Accurate traffic prediction is a key ingredient to enable traffic management like rerouting cars to reduce road congestion or regulating traffic via dynamic speed limits to maintain a steady flow.
YOLO -- You only look 10647 times
With this work we are explaining the "You Only Look Once" (YOLO) single-stage object detection approach as a parallel classification of 10647 fixed region proposals.
Transfer Learning with Jukebox for Music Source Separation
In this work, we demonstrate how a publicly available, pre-trained Jukebox model can be adapted for the problem of audio source separation from a single mixed audio channel.
Ranked #1 on
Music Source Separation
on MUSDB18-HQ
Critic Guided Segmentation of Rewarding Objects in First-Person Views
This work discusses a learning approach to mask rewarding objects in images using sparse reward signals from an imitation learning dataset.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Solving Physics Puzzles by Reasoning about Paths
We propose a new deep learning model for goal-driven tasks that require intuitive physical reasoning and intervention in the scene to achieve a desired end goal.
Modularization of End-to-End Learning: Case Study in Arcade Games
Complex environments and tasks pose a difficult problem for holistic end-to-end learning approaches.
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments
In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course.
