1.6k citations · 4.4k across the 137 of their papers we have counts for
6 papers · 1 filter
MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Jiajun Wu, Yifan Wang, Tianfan Xue +3
3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenges for learning-…
Self-Supervised Intrinsic Image Decomposition
Michael Janner, Jiajun Wu, Tejas D. Kulkarni +2
Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervis…
A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions
Andres Campero, Bjarke Felbo, Joshua B. Tenenbaum +1
We explore the representational space of emotions by combining methods from different academic fields. Cognitive science has proposed appraisal theory as a view on human emotion wi…
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
Ilker Yildirim, Tobias Gerstenberg, Basil Saeed +2
In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series…
Learning to Infer Graphics Programs from Hand-Drawn Images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama +1
We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program…
Beating the World's Best at Super Smash Bros. with Deep Reinforcement Learning
Vlad Firoiu, William F. Whitney, Joshua B. Tenenbaum
There has been a recent explosion in the capabilities of game-playing artificial intelligence. Many classes of RL tasks, from Atari games to motor control to board games, are now s…