1.6k citations · 9.7k across the 222 of their papers we have counts for
41 papers · 1 filter
Are Deep Neural Networks SMARTer than Second Graders?
Anoop Cherian, Kuan-Chuan Peng, Suhas Lohit +2
Recent times have witnessed an increasing number of applications of deep neural networks towards solving tasks that require superior cognitive abilities, e.g., playing Go, generati…
Flexible social inference facilitates targeted social learning when rewards are not observable
Robert D. Hawkins, Andrew M. Berdahl, Alex "Sandy" Pentland +3
Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments…
On the Complexity of Bayesian Generalization
Yu-Zhe Shi, Manjie Xu, John E. Hopcroft +6
We consider concept generalization at a large scale in the diverse and natural visual spectrum. Established computational modes (i.e., rule-based or similarity-based) are primarily…
Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning
Aviv Netanyahu, Tianmin Shu, Joshua Tenenbaum +1
In this work, we consider one-shot imitation learning for object rearrangement tasks, where an AI agent needs to watch a single expert demonstration and learn to perform the same t…
Top-Down Synthesis for Library Learning
Matthew Bowers, Theo X. Olausson, Lionel Wong +4
This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain spe…
Is Conditional Generative Modeling all you need for Decision-Making?
Anurag Ajay, Yilun Du, Abhi Gupta +3
Recent improvements in conditional generative modeling have made it possible to generate high-quality images from language descriptions alone. We investigate whether these methods…
When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment
Zhijing Jin, Sydney Levine, Fernando Gonzalez +6
AI systems are becoming increasingly intertwined with human life. In order to effectively collaborate with humans and ensure safety, AI systems need to be able to understand, inter…
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith +5
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects…
Composing Ensembles of Pre-trained Models via Iterative Consensus
Shuang Li, Yilun Du, Joshua B. Tenenbaum +2
Large pre-trained models exhibit distinct and complementary capabilities dependent on the data they are trained on. Language models such as GPT-3 are capable of textual reasoning b…
Revisiting the Roles of "Text" in Text Games
Yi Gu, Shunyu Yao, Chuang Gan +2
Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessit…
Learning Physical Dynamics with Subequivariant Graph Neural Networks
Jiaqi Han, Wenbing Huang, Hengbo Ma +3
Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. However, they still encounter several challenges: 1) Physical laws abide by symmetry, whi…
Retrospectives on the Embodied AI Workshop
Matt Deitke, Dhruv Batra, Yonatan Bisk +36
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped i…
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models. We illustrate ho…
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok +3
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102.11938) prov…
Robust Change Detection Based on Neural Descriptor Fields
Jiahui Fu, Yilun Du, Kurran Singh +2
The ability to reason about changes in the environment is crucial for robots operating over extended periods of time. Agents are expected to capture changes during operation so tha…
3D Concept Grounding on Neural Fields
Yining Hong, Yilun Du, Chunru Lin +2
In this paper, we address the challenging problem of 3D concept grounding (i.e. segmenting and learning visual concepts) by looking at RGBD images and reasoning about paired questi…
Finding Fallen Objects Via Asynchronous Audio-Visual Integration
Chuang Gan, Yi Gu, Siyuan Zhou +7
The way an object looks and sounds provide complementary reflections of its physical properties. In many settings cues from vision and audition arrive asynchronously but must be in…
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Prafull Sharma, Ayush Tewari, Yilun Du +7
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable an…
Learning Iterative Reasoning through Energy Minimization
Yilun Du, Shuang Li, Joshua B. Tenenbaum +1
Deep learning has excelled on complex pattern recognition tasks such as image classification and object recognition. However, it struggles with tasks requiring nontrivial reasoning…
Prompting Decision Transformer for Few-Shot Policy Generalization
Mengdi Xu, Yikang Shen, Shun Zhang +4
Humans can leverage prior experience and learn novel tasks from a handful of demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve quick adaptat…
Learning Neuro-Symbolic Skills for Bilevel Planning
Tom Silver, Ashay Athalye, Joshua B. Tenenbaum +2
Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such…
Compositional Visual Generation with Composable Diffusion Models
Nan Liu, Shuang Li, Yilun Du +2
Large text-guided diffusion models, such as DALLE-2, are able to generate stunning photorealistic images given natural language descriptions. While such models are highly flexible,…
Structured, flexible, and robust: benchmarking and improving large language models towards more human-like behavior in out-of-distribution reasoning tasks
Katherine M. Collins, Catherine Wong, Jiahai Feng +2
Human language offers a powerful window into our thoughts -- we tell stories, give explanations, and express our beliefs and goals through words. Abundant evidence also suggests th…
RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation
Pingchuan Ma, Tao Du, Joshua B. Tenenbaum +2
This work considers identifying parameters characterizing a physical system's dynamic motion directly from a video whose rendering configurations are inaccessible. Existing solutio…