Search Results for author: Joshua B. Tenenbaum

Found 167 papers, 48 papers with code

Unsupervised Segmentation in Real-World Images via Spelke Object Inference

no code implementations17 May 2022 Honglin Chen, Rahul Venkatesh, Yoni Friedman, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins, Daniel M. Bear

We show that EISEN achieves a substantial improvement in the state of the art for self-supervised segmentation on challenging synthetic and real-world robotic image datasets.

Optical Flow Estimation

Identifying concept libraries from language about object structure

no code implementations11 May 2022 Catherine Wong, William P. McCarthy, Gabriel Grand, Yoni Friedman, Joshua B. Tenenbaum, Jacob Andreas, Robert D. Hawkins, Judith E. Fan

Our understanding of the visual world goes beyond naming objects, encompassing our ability to parse objects into meaningful parts, attributes, and relations.

Machine Translation Translation

Unsupervised Discovery and Composition of Object Light Fields

no code implementations8 May 2022 Cameron Smith, Hong-Xing Yu, Sergey Zakharov, Fredo Durand, Joshua B. Tenenbaum, Jiajun Wu, Vincent Sitzmann

Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.

Novel View Synthesis Scene Understanding

Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

no code implementations ICLR 2022 Sizhe Li, Zhiao Huang, Tao Du, Hao Su, Joshua B. Tenenbaum, Chuang Gan

Extensive experimental results suggest that: 1) on multi-stage tasks that are infeasible for the vanilla differentiable physics solver, our approach discovers contact points that efficiently guide the solver to completion; 2) on tasks where the vanilla solver performs sub-optimally or near-optimally, our contact point discovery method performs better than or on par with the manipulation performance obtained with handcrafted contact points.

Fixing Malfunctional Objects With Learned Physical Simulation and Functional Prediction

no code implementations5 May 2022 Yining Hong, Kaichun Mo, Li Yi, Leonidas J. Guibas, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan

Specifically, FixNet consists of a perception module to extract the structured representation from the 3D point cloud, a physical dynamics prediction module to simulate the results of interactions on 3D objects, and a functionality prediction module to evaluate the functionality and choose the correct fix.

ComPhy: Compositional Physical Reasoning of Objects and Events from Videos

no code implementations ICLR 2022 Zhenfang Chen, Kexin Yi, Yunzhu Li, Mingyu Ding, Antonio Torralba, Joshua B. Tenenbaum, Chuang Gan

In this paper, we take an initial step to highlight the importance of inferring the hidden physical properties not directly observable from visual appearances, by introducing the Compositional Physical Reasoning (ComPhy) dataset.

Learning Neural Acoustic Fields

no code implementations4 Apr 2022 Andrew Luo, Yilun Du, Michael J. Tarr, Joshua B. Tenenbaum, Antonio Torralba, Chuang Gan

By modeling acoustic propagation in a scene as a linear time-invariant system, NAFs learn to continuously map all emitter and listener location pairs to a neural impulse response function that can then be applied to arbitrary sounds.

FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations

no code implementations ICLR 2022 Lingjie Mei, Jiayuan Mao, Ziqi Wang, Chuang Gan, Joshua B. Tenenbaum

We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at images, reading sentences that describe the objects in the scene, and interpreting supplemental sentences that relate the novel concept with other concepts.

Meta-Learning

Linking Emergent and Natural Languages via Corpus Transfer

1 code implementation ICLR 2022 Shunyu Yao, Mo Yu, Yang Zhang, Karthik R Narasimhan, Joshua B. Tenenbaum, Chuang Gan

In this work, we propose a novel way to establish such a link by corpus transfer, i. e. pretraining on a corpus of emergent language for downstream natural language tasks, which is in contrast to prior work that directly transfers speaker and listener parameters.

Disentanglement Image Captioning +1

Grammar-Based Grounded Lexicon Learning

no code implementations NeurIPS 2021 Jiayuan Mao, Haoyue Shi, Jiajun Wu, Roger P. Levy, Joshua B. Tenenbaum

We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a compositional and grounded meaning representation of language from grounded data, such as paired images and texts.

Network Embedding Visual Reasoning

Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation

no code implementations9 Dec 2021 Anthony Simeonov, Yilun Du, Andrea Tagliasacchi, Joshua B. Tenenbaum, Alberto Rodriguez, Pulkit Agrawal, Vincent Sitzmann

Our performance generalizes across both object instances and 6-DoF object poses, and significantly outperforms a recent baseline that relies on 2D descriptors.

PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning

no code implementations NeurIPS 2021 Yining Hong, Li Yi, Joshua B. Tenenbaum, Antonio Torralba, Chuang Gan

A critical aspect of human visual perception is the ability to parse visual scenes into individual objects and further into object parts, forming part-whole hierarchies.

Instance Segmentation Semantic Segmentation +1

STAR: A Benchmark for Situated Reasoning in Real-World Videos

1 code implementation NeurIPS 2021 Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B. Tenenbaum, Chuang Gan

This paper introduces a new benchmark that evaluates the situated reasoning ability via situation abstraction and logic-grounded question answering for real-world videos, called Situated Reasoning in Real-World Videos (STAR).

Question Answering

Learning to Compose Visual Relations

no code implementations NeurIPS 2021 Nan Liu, Shuang Li, Yilun Du, Joshua B. Tenenbaum, Antonio Torralba

The visual world around us can be described as a structured set of objects and their associated relations.

Learning Signal-Agnostic Manifolds of Neural Fields

no code implementations NeurIPS 2021 Yilun Du, Katherine M. Collins, Joshua B. Tenenbaum, Vincent Sitzmann

We leverage neural fields to capture the underlying structure in image, shape, audio and cross-modal audiovisual domains in a modality-independent manner.

Unsupervised Learning of Compositional Energy Concepts

1 code implementation NeurIPS 2021 Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch

In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework.

Disentanglement Unsupervised Image Decomposition

Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language

no code implementations NeurIPS 2021 Mingyu Ding, Zhenfang Chen, Tao Du, Ping Luo, Joshua B. Tenenbaum, Chuang Gan

This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.

Visual Reasoning

OPEn: An Open-ended Physics Environment for Learning Without a Task

no code implementations13 Oct 2021 Chuang Gan, Abhishek Bhandwaldar, Antonio Torralba, Joshua B. Tenenbaum, Phillip Isola

We test several existing RL-based exploration methods on this benchmark and find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results.

Contrastive Learning Representation Learning

AutumnSynth: Synthesis of Reactive Programs with Structured Latent State

no code implementations NeurIPS Workshop AIPLANS 2021 Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavares

The human ability to efficiently discover causal theories of their environments from observations is a feat of nature that remains elusive in machines.

Program Synthesis

On the Expressiveness and Learning of Relational Neural Networks on Hypergraphs

no code implementations29 Sep 2021 Zhezheng Luo, Jiayuan Mao, Joshua B. Tenenbaum, Leslie Pack Kaelbling

Our first contribution is a fine-grained analysis of the expressiveness of these neural networks, that is, the set of functions that they can realize and the set of problems that they can solve.

Inducing Reusable Skills From Demonstrations with Option-Controller Network

no code implementations29 Sep 2021 Siyuan Zhou, Yikang Shen, Yuchen Lu, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

With the isolation of information and the synchronous calling mechanism, we can impose a division of works between the controller and options in an end-to-end training regime.

Learning Rational Skills for Planning from Demonstrations and Instructions

no code implementations29 Sep 2021 Zhezheng Luo, Jiayuan Mao, Jiajun Wu, Tomas Perez, Joshua B. Tenenbaum, Leslie Pack Kaelbling

We present a framework for learning compositional, rational skill models (RatSkills) that support efficient planning and inverse planning for achieving novel goals and recognizing activities.

Dynamic Modeling of Hand-Object Interactions via Tactile Sensing

no code implementations9 Sep 2021 Qiang Zhang, Yunzhu Li, Yiyue Luo, Wan Shou, Michael Foshey, Junchi Yan, Joshua B. Tenenbaum, Wojciech Matusik, Antonio Torralba

This work takes a step on dynamics modeling in hand-object interactions from dense tactile sensing, which opens the door for future applications in activity learning, human-computer interactions, and imitation learning for robotics.

Contrastive Learning Imitation Learning

Learning to solve complex tasks by growing knowledge culturally across generations

1 code implementation28 Jul 2021 Michael Henry Tessler, Jason Madeano, Pedro A. Tsividis, Brin Harper, Noah D. Goodman, Joshua B. Tenenbaum

The video game paradigm we pioneer here is thus a rich test bed for developing AI systems capable of acquiring and transmitting cultural knowledge.

Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning

no code implementations27 Jul 2021 Pedro A. Tsividis, Joao Loula, Jake Burga, Nathan Foss, Andres Campero, Thomas Pouncy, Samuel J. Gershman, Joshua B. Tenenbaum

Here we propose a new approach to this challenge based on a particularly strong form of model-based RL which we call Theory-Based Reinforcement Learning, because it uses human-like intuitive theories -- rich, abstract, causal models of physical objects, intentional agents, and their interactions -- to explore and model an environment, and plan effectively to achieve task goals.

Bayesian Inference Board Games +1

Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning

no code implementations NeurIPS 2021 Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum, Brenden M. Lake

Human reasoning can often be understood as an interplay between two systems: the intuitive and associative ("System 1") and the deliberative and logical ("System 2").

Story Generation

Hybrid Memoised Wake-Sleep: Approximate Inference at the Discrete-Continuous Interface

no code implementations ICLR 2022 Tuan Anh Le, Katherine M. Collins, Luke Hewitt, Kevin Ellis, N. Siddharth, Samuel J. Gershman, Joshua B. Tenenbaum

We build on a recent approach, Memoised Wake-Sleep (MWS), which alleviates part of the problem by memoising discrete variables, and extend it to allow for a principled and effective way to handle continuous variables by learning a separate recognition model used for importance-sampling based approximate inference and marginalization.

Scene Understanding Time Series

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind

no code implementations24 Jun 2021 Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash K. Mansinghka, Joshua B. Tenenbaum

Here, we extend the Bayesian Theory of Mind framework to model boundedly rational agents who may have mistaken goals, plans, and actions.

Game of Chess

Leveraging Language to Learn Program Abstractions and Search Heuristics

no code implementations18 Jun 2021 Catherine Wong, Kevin Ellis, Joshua B. Tenenbaum, Jacob Andreas

Inductive program synthesis, or inferring programs from examples of desired behavior, offers a general paradigm for building interpretable, robust, and generalizable machine learning systems.

Program Synthesis

Communicating Natural Programs to Humans and Machines

1 code implementation15 Jun 2021 Samuel Acquaviva, Yewen Pu, Marta Kryven, Theodoros Sechopoulos, Catherine Wong, Gabrielle E Ecanow, Maxwell Nye, Michael Henry Tessler, Joshua B. Tenenbaum

We present LARC, the \textit{Language-complete ARC}: a collection of natural language descriptions by a group of human participants who instruct each other on how to solve ARC tasks using language alone, which contains successful instructions for 88\% of the ARC tasks.

Program Synthesis

Physion: Evaluating Physical Prediction from Vision in Humans and Machines

3 code implementations15 Jun 2021 Daniel M. Bear, Elias Wang, Damian Mrowca, Felix J. Binder, Hsiau-Yu Fish Tung, R. T. Pramod, Cameron Holdaway, Sirui Tao, Kevin Smith, Fan-Yun Sun, Li Fei-Fei, Nancy Kanwisher, Joshua B. Tenenbaum, Daniel L. K. Yamins, Judith E. Fan

While machine learning algorithms excel at many challenging visual tasks, it is unclear that they can make predictions about commonplace real world physical events.

Temporal and Object Quantification Networks

no code implementations10 Jun 2021 Jiayuan Mao, Zhezheng Luo, Chuang Gan, Joshua B. Tenenbaum, Jiajun Wu, Leslie Pack Kaelbling, Tomer D. Ullman

We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex relational-temporal events.

Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering

1 code implementation NeurIPS 2021 Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, Fredo Durand

In this work, we propose a novel neural scene representation, Light Field Networks or LFNs, which represent both geometry and appearance of the underlying 3D scene in a 360-degree, four-dimensional light field parameterized via a neural implicit representation.

Meta-Learning Scene Understanding

PlasticineLab: A Soft-Body Manipulation Benchmark with Differentiable Physics

1 code implementation ICLR 2021 Zhiao Huang, Yuanming Hu, Tao Du, Siyuan Zhou, Hao Su, Joshua B. Tenenbaum, Chuang Gan

Experimental results suggest that 1) RL-based approaches struggle to solve most of the tasks efficiently; 2) gradient-based approaches, by optimizing open-loop control sequences with the built-in differentiable physics engine, can rapidly find a solution within tens of iterations, but still fall short on multi-stage tasks that require long-term planning.

Learning Task Decomposition with Ordered Memory Policy Network

no code implementations19 Mar 2021 Yuchen Lu, Yikang Shen, Siyuan Zhou, Aaron Courville, Joshua B. Tenenbaum, Chuang Gan

The discovered subtask hierarchy could be used to perform task decomposition, recovering the subtask boundaries in an unstruc-tured demonstration.

PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception

no code implementations NeurIPS Workshop SVRHM 2020 Aviv Netanyahu, Tianmin Shu, Boris Katz, Andrei Barbu, Joshua B. Tenenbaum

The ability to perceive and reason about social interactions in the context of physical environments is core to human social intelligence and human-machine cooperation.

AGENT: A Benchmark for Core Psychological Reasoning

no code implementations24 Feb 2021 Tianmin Shu, Abhishek Bhandwaldar, Chuang Gan, Kevin A. Smith, Shari Liu, Dan Gutfreund, Elizabeth Spelke, Joshua B. Tenenbaum, Tomer D. Ullman

For machine agents to successfully interact with humans in real-world settings, they will need to develop an understanding of human mental life.

Core Psychological Reasoning

A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics

no code implementations1 Jan 2021 Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer Ullman, Joshua B. Tenenbaum, Charles Sutton

As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.

Bayesian Inference Common Sense Reasoning

Temporal and Object Quantification Nets

no code implementations1 Jan 2021 Jiayuan Mao, Zhezheng Luo, Chuang Gan, Joshua B. Tenenbaum, Jiajun Wu, Leslie Pack Kaelbling, Tomer Ullman

We aim to learn generalizable representations for complex activities by quantifying over both entities and time, as in “the kicker is behind all the other players,” or “the player controls the ball until it moves toward the goal.” Such a structural inductive bias of object relations, object quantification, and temporal orders will enable the learned representation to generalize to situations with varying numbers of agents, objects, and time courses.

Event Detection

Representing Partial Programs with Blended Abstract Semantics

no code implementations ICLR 2021 Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama

In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to judge if it is on the right track and predict where to search next.

Program Synthesis

Object-Centric Diagnosis of Visual Reasoning

no code implementations21 Dec 2020 Jianwei Yang, Jiayuan Mao, Jiajun Wu, Devi Parikh, David D. Cox, Joshua B. Tenenbaum, Chuang Gan

In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy.

Question Answering Visual Question Answering +1

Neural Radiance Flow for 4D View Synthesis and Video Processing

1 code implementation ICCV 2021 Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B. Tenenbaum, Jiajun Wu

We present a method, Neural Radiance Flow (NeRFlow), to learn a 4D spatial-temporal representation of a dynamic scene from a set of RGB images.

Image Super-Resolution

Multi-Plane Program Induction with 3D Box Priors

no code implementations NeurIPS 2020 Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Noah Snavely, Jiajun Wu

We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene.

Program induction Program Synthesis

Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation

no code implementations14 Nov 2020 Kei Ota, Devesh K. Jha, Diego Romeres, Jeroen van Baar, Kevin A. Smith, Takayuki Semitsu, Tomoaki Oiki, Alan Sullivan, Daniel Nikovski, Joshua B. Tenenbaum

The physics engine augmented with the residual model is then used to control the marble in the maze environment using a model-predictive feedback over a receding horizon.

reinforcement-learning

Causal Inductive Synthesis Corpus

no code implementations NeurIPS Workshop CAP 2020 Zenna Tavares, Ria Das, Elizabeth Weeks, Kate Lin, Joshua B. Tenenbaum, Armando Solar-Lezama

We introduce the Causal Inductive Synthesis Corpus (CISC) -- a manually constructed collection of interactive domains.

Model Discovery

Measuring few-shot extrapolation with program induction

no code implementations NeurIPS Workshop CAP 2020 Ferran Alet, Javier Lopez-Contreras, Joshua B. Tenenbaum, Tomas Perez, Leslie Pack Kaelbling

Program induction lies at the opposite end of the spectrum: programs are capable of extrapolating from very few examples, but we still do not know how to efficiently search for complex programs.

Meta-Learning Program induction

Learning Online Data Association

no code implementations28 Sep 2020 Yilun Du, Joshua B. Tenenbaum, Tomas Perez, Leslie Pack Kaelbling

When an agent interacts with a complex environment, it receives a stream of percepts in which it may detect entities, such as objects or people.

Representation Learning

Noisy Agents: Self-supervised Exploration by Predicting Auditory Events

no code implementations27 Jul 2020 Chuang Gan, Xiaoyu Chen, Phillip Isola, Antonio Torralba, Joshua B. Tenenbaum

Humans integrate multiple sensory modalities (e. g. visual and audio) to build a causal understanding of the physical world.

Atari Games

End-to-End Optimization of Scene Layout

1 code implementation CVPR 2020 Andrew Luo, Zhoutong Zhang, Jiajun Wu, Joshua B. Tenenbaum

Experiments suggest that our model achieves higher accuracy and diversity in conditional scene synthesis and allows exemplar-based scene generation from various input forms.

Indoor Scene Reconstruction Indoor Scene Synthesis +2

Foley Music: Learning to Generate Music from Videos

no code implementations ECCV 2020 Chuang Gan, Deng Huang, Peihao Chen, Joshua B. Tenenbaum, Antonio Torralba

In this paper, we introduce Foley Music, a system that can synthesize plausible music for a silent video clip about people playing musical instruments.

Music Generation Translation

Learning to learn generative programs with Memoised Wake-Sleep

no code implementations6 Jul 2020 Luke B. Hewitt, Tuan Anh Le, Joshua B. Tenenbaum

We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs.

Explainable Models Few-Shot Learning +1

Learning Physical Graph Representations from Visual Scenes

1 code implementation NeurIPS 2020 Daniel M. Bear, Chaofei Fan, Damian Mrowca, Yunzhu Li, Seth Alter, Aran Nayebi, Jeremy Schwartz, Li Fei-Fei, Jiajun Wu, Joshua B. Tenenbaum, Daniel L. K. Yamins

To overcome these limitations, we introduce the idea of Physical Scene Graphs (PSGs), which represent scenes as hierarchical graphs, with nodes in the hierarchy corresponding intuitively to object parts at different scales, and edges to physical connections between parts.

Object Categorization Scene Segmentation

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning

2 code implementations15 Jun 2020 Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum

It builds expertise by creating programming languages for expressing domain concepts, together with neural networks to guide the search for programs within these languages.

Drawing Pictures Program induction +1

Online Bayesian Goal Inference for Boundedly-Rational Planning Agents

1 code implementation13 Jun 2020 Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Joshua B. Tenenbaum, Vikash K. Mansinghka

These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.

Bayesian Inference

Deep Audio Priors Emerge From Harmonic Convolutional Networks

no code implementations ICLR 2020 Zhoutong Zhang, Yunyun Wang, Chuang Gan, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman

We show that networks using Harmonic Convolution can reliably model audio priors and achieve high performance in unsupervised audio restoration tasks.

Visual Grounding of Learned Physical Models

no code implementations ICML 2020 Yunzhu Li, Toru Lin, Kexin Yi, Daniel M. Bear, Daniel L. K. Yamins, Jiajun Wu, Joshua B. Tenenbaum, Antonio Torralba

The abilities to perform physical reasoning and to adapt to new environments, while intrinsic to humans, remain challenging to state-of-the-art computational models.

Visual Grounding

Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

no code implementations20 Apr 2020 Yixin Zhu, Tao Gao, Lifeng Fan, Siyuan Huang, Mark Edmonds, Hangxin Liu, Feng Gao, Chi Zhang, Siyuan Qi, Ying Nian Wu, Joshua B. Tenenbaum, Song-Chun Zhu

We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.

Common Sense Reasoning Small Data Image Classification

Too many cooks: Bayesian inference for coordinating multi-agent collaboration

no code implementations26 Mar 2020 Rose E. Wang, Sarah A. Wu, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner

Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act.

Bayesian Inference

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

3 code implementations ECCV 2020 Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B. Tenenbaum, Phillip Isola

The focus of recent meta-learning research has been on the development of learning algorithms that can quickly adapt to test time tasks with limited data and low computational cost.

Few-Shot Image Classification General Classification

Visual Concept-Metaconcept Learning

1 code implementation NeurIPS 2019 Chi Han, Jiayuan Mao, Chuang Gan, Joshua B. Tenenbaum, Jiajun Wu

Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i. e., the color).

Look, Listen, and Act: Towards Audio-Visual Embodied Navigation

1 code implementation25 Dec 2019 Chuang Gan, Yiwei Zhang, Jiajun Wu, Boqing Gong, Joshua B. Tenenbaum

In this paper, we attempt to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data.

Entity Abstraction in Visual Model-Based Reinforcement Learning

1 code implementation28 Oct 2019 Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine

This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit in generalizing to physical tasks in a combinatorial space the learner has not encountered before.

Model-based Reinforcement Learning Object Discovery +3

CLEVRER: CoLlision Events for Video REpresentation and Reasoning

3 code implementations ICLR 2020 Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, Joshua B. Tenenbaum

While these models thrive on the perception-based task (descriptive), they perform poorly on the causal tasks (explanatory, predictive and counterfactual), suggesting that a principled approach for causal reasoning should incorporate the capability of both perceiving complex visual and language inputs, and understanding the underlying dynamics and causal relations.

Visual Reasoning

DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs

1 code implementation28 Sep 2019 Yunbo Wang, Bo Liu, Jiajun Wu, Yuke Zhu, Simon S. Du, Li Fei-Fei, Joshua B. Tenenbaum

A major difficulty of solving continuous POMDPs is to infer the multi-modal distribution of the unobserved true states and to make the planning algorithm dependent on the perceived uncertainty.

Continuous Control

Program-Guided Image Manipulators

no code implementations ICCV 2019 Jiayuan Mao, Xiuming Zhang, Yikai Li, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

Humans are capable of building holistic representations for images at various levels, from local objects, to pairwise relations, to global structures.

Image Inpainting

Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning

no code implementations22 Jul 2019 Kelsey R. Allen, Kevin A. Smith, Joshua B. Tenenbaum

But human beings remain distinctive in their capacity for flexible, creative tool use -- using objects in new ways to act on the world, achieve a goal, or solve a problem.

Neurally-Guided Structure Inference

no code implementations17 Jun 2019 Sidi Lu, Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu

In this paper, we propose a hybrid inference algorithm, the Neurally-Guided Structure Inference (NG-SI), keeping the advantages of both search-based and data-driven methods.

Finding Friend and Foe in Multi-Agent Games

1 code implementation NeurIPS 2019 Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Joshua B. Tenenbaum

Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game.

Multi-agent Reinforcement Learning

Predicting the Present and Future States of Multi-agent Systems from Partially-observed Visual Data

no code implementations ICLR 2019 Chen Sun, Per Karlsson, Jiajun Wu, Joshua B. Tenenbaum, Kevin Murphy

We present a method which learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.

Modeling Parts, Structure, and System Dynamics via Predictive Learning

no code implementations ICLR 2019 Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.

Learning to Describe Scenes with Programs

no code implementations ICLR 2019 Yunchao Liu, Zheng Wu, Daniel Ritchie, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

We are able to understand the higher-level, abstract regularities within the scene such as symmetry and repetition.

Combining Physical Simulators and Object-Based Networks for Control

no code implementations13 Apr 2019 Anurag Ajay, Maria Bauza, Jiajun Wu, Nima Fazeli, Joshua B. Tenenbaum, Alberto Rodriguez, Leslie P. Kaelbling

Physics engines play an important role in robot planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically.

Unsupervised Discovery of Parts, Structure, and Dynamics

no code implementations12 Mar 2019 Zhenjia Xu, Zhijian Liu, Chen Sun, Kevin Murphy, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future.

Stochastic Prediction of Multi-Agent Interactions from Partial Observations

no code implementations25 Feb 2019 Chen Sun, Per Karlsson, Jiajun Wu, Joshua B. Tenenbaum, Kevin Murphy

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents.

Infinite Mixture Prototypes for Few-Shot Learning

no code implementations12 Feb 2019 Kelsey R. Allen, Evan Shelhamer, Hanul Shin, Joshua B. Tenenbaum

We propose infinite mixture prototypes to adaptively represent both simple and complex data distributions for few-shot learning.

Few-Shot Learning

The Omniglot challenge: a 3-year progress report

7 code implementations9 Feb 2019 Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum

Three years ago, we released the Omniglot dataset for one-shot learning, along with five challenge tasks and a computational model that addresses these tasks.

General Classification One-Shot Learning

On the Units of GANs (Extended Abstract)

no code implementations29 Jan 2019 David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B. Tenenbaum, William T. Freeman, Antonio Torralba

We quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output.

Learning to Infer and Execute 3D Shape Programs

no code implementations ICLR 2019 Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts.

Learning to Reconstruct Shapes from Unseen Classes

no code implementations NeurIPS 2018 Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu

From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life.

3D Reconstruction

Library Learning for Neurally-Guided Bayesian Program Induction

no code implementations1 Dec 2018 Kevin Ellis, Lucas Morales, Mathias Sablé-Meyer, Armando Solar-Lezama, Joshua B. Tenenbaum

Successful approaches to program induction require a hand-engineered domain-specific language (DSL), constraining the space of allowed programs and imparting prior knowledge of the domain.

Program induction

Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding

1 code implementation NeurIPS 2018 Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, Joshua B. Tenenbaum

Second, the model is more data- and memory-efficient: it performs well after learning on a small number of training data; it can also encode an image into a compact representation, requiring less storage than existing methods for offline question answering.

Question Answering Representation Learning +2

ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics

no code implementations2 Oct 2018 Yuanming Hu, Jian-Cheng Liu, Andrew Spielberg, Joshua B. Tenenbaum, William T. Freeman, Jiajun Wu, Daniela Rus, Wojciech Matusik

The underlying physical laws of deformable objects are more complex, and the resulting systems have orders of magnitude more degrees of freedom and therefore they are significantly more computationally expensive to simulate.

Motion Planning

Seeing Tree Structure from Vibration

no code implementations ECCV 2018 Tianfan Xue, Jiajun Wu, Zhoutong Zhang, Chengkai Zhang, Joshua B. Tenenbaum, William T. Freeman

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only.

Bayesian Inference

Learning Shape Priors for Single-View 3D Completion and Reconstruction

no code implementations ECCV 2018 Jiajun Wu, Chengkai Zhang, Xiuming Zhang, Zhoutong Zhang, William T. Freeman, Joshua B. Tenenbaum

The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects.

Physical Primitive Decomposition

no code implementations ECCV 2018 Zhijian Liu, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu

As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events.

Modeling human intuitions about liquid flow with particle-based simulation

no code implementations5 Sep 2018 Christopher J. Bates, Ilker Yildirim, Joshua B. Tenenbaum, Peter Battaglia

Humans can easily describe, imagine, and, crucially, predict a wide variety of behaviors of liquids--splashing, squirting, gushing, sloshing, soaking, dripping, draining, trickling, pooling, and pouring--despite tremendous variability in their material and dynamical properties.

Scene Understanding

3D-Aware Scene Manipulation via Inverse Graphics

1 code implementation NeurIPS 2018 Shunyu Yao, Tzu Ming Harry Hsu, Jun-Yan Zhu, Jiajun Wu, Antonio Torralba, William T. Freeman, Joshua B. Tenenbaum

In this work, we propose 3D scene de-rendering networks (3D-SDN) to address the above issues by integrating disentangled representations for semantics, geometry, and appearance into a deep generative model.

Disentanglement

Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing

no code implementations9 Aug 2018 Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P. Kaelbling, Joshua B. Tenenbaum, Alberto Rodriguez

An efficient, generalizable physical simulator with universal uncertainty estimates has wide applications in robot state estimation, planning, and control.

Gaussian Processes

The Variational Homoencoder: Learning to learn high capacity generative models from few examples

1 code implementation24 Jul 2018 Luke B. Hewitt, Maxwell I. Nye, Andreea Gane, Tommi Jaakkola, Joshua B. Tenenbaum

However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typically fail to effectively use latent variables.

General Classification

Unsupervised Learning of Latent Physical Properties Using Perception-Prediction Networks

no code implementations24 Jul 2018 David Zheng, Vinson Luo, Jiajun Wu, Joshua B. Tenenbaum

We propose a framework for the completely unsupervised learning of latent object properties from their interactions: the perception-prediction network (PPN).

Flexible Neural Representation for Physics Prediction

no code implementations NeurIPS 2018 Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins

Humans have a remarkable capacity to understand the physical dynamics of objects in their environment, flexibly capturing complex structures and interactions at multiple levels of detail.

Relational inductive bias for physical construction in humans and machines

no code implementations4 Jun 2018 Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia

While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.

reinforcement-learning

Word learning and the acquisition of syntactic--semantic overhypotheses

no code implementations14 May 2018 Jon Gauthier, Roger Levy, Joshua B. Tenenbaum

Children learning their first language face multiple problems of induction: how to learn the meanings of words, and how to build meaningful phrases from those words according to syntactic rules.

Language Acquisition

Discovery and usage of joint attention in images

no code implementations10 Apr 2018 Daniel Harari, Joshua B. Tenenbaum, Shimon Ullman

Second, we use a human study to demonstrate the sensitivity of humans to joint attention, suggesting that the detection of such a configuration in an image can be useful for understanding the image, including the goals of the agents and their joint activity, and therefore can contribute to image captioning and related tasks.

Image Captioning

3D Interpreter Networks for Viewer-Centered Wireframe Modeling

no code implementations3 Apr 2018 Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman

3D-INN is trained on real images to estimate 2D keypoint heatmaps from an input image; it then predicts 3D object structure from heatmaps using knowledge learned from synthetic 3D shapes.

Image Retrieval

The Three Pillars of Machine Programming

no code implementations20 Mar 2018 Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, Michael Carbin, Martin Rinard, Regina Barzilay, Saman Amarasinghe, Joshua B. Tenenbaum, Tim Mattson

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research.

Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation

no code implementations19 Mar 2018 Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B. Tenenbaum, Azim Shariff, Jean-François Bonnefon, Iyad Rahwan

However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced.

Meta-Learning for Semi-Supervised Few-Shot Classification

8 code implementations ICLR 2018 Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel

To address this paradigm, we propose novel extensions of Prototypical Networks (Snell et al., 2017) that are augmented with the ability to use unlabeled examples when producing prototypes.

Classification General Classification +1

The Variational Homoencoder: Learning to Infer High-Capacity Generative Models from Few Examples

no code implementations ICLR 2018 Luke Hewitt, Andrea Gane, Tommi Jaakkola, Joshua B. Tenenbaum

Hierarchical Bayesian methods have the potential to unify many related tasks (e. g. k-shot classification, conditional, and unconditional generation) by framing each as inference within a single generative model.

General Classification

Self-Supervised Intrinsic Image Decomposition

no code implementations NeurIPS 2017 Michael Janner, Jiajun Wu, Tejas D. Kulkarni, Ilker Yildirim, Joshua B. Tenenbaum

Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data.

Intrinsic Image Decomposition Transfer Learning

MarrNet: 3D Shape Reconstruction via 2.5D Sketches

no code implementations NeurIPS 2017 Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, William T. Freeman, Joshua B. Tenenbaum

First, compared to full 3D shape, 2. 5D sketches are much easier to be recovered from a 2D image; models that recover 2. 5D sketches are also more likely to transfer from synthetic to real data.

3D Object Reconstruction From A Single Image 3D Reconstruction +2

A First Step in Combining Cognitive Event Features and Natural Language Representations to Predict Emotions

no code implementations23 Oct 2017 Andres Campero, Bjarke Felbo, Joshua B. Tenenbaum, Rebecca Saxe

Cognitive science has proposed appraisal theory as a view on human emotion with previous research showing how human-rated abstract event features can predict fine-grained emotions and capture the similarity space of neural patterns in mentalizing brain regions.

Synthesizing 3D Shapes via Modeling Multi-View Depth Maps and Silhouettes With Deep Generative Networks

no code implementations CVPR 2017 Amir Arsalan Soltani, Haibin Huang, Jiajun Wu, Tejas D. Kulkarni, Joshua B. Tenenbaum

We take an alternative approach: learning a generative model over multi-view depth maps or their corresponding silhouettes, and using a deterministic rendering function to produce 3D shapes from these images.

Neural Scene De-Rendering

no code implementations CVPR 2017 Jiajun Wu, Joshua B. Tenenbaum, Pushmeet Kohli

Our approach employs a deterministic rendering function as the decoder, mapping a naturally structured and disentangled scene description, which we named scene XML, to an image.

Image Captioning Scene Understanding

A Compositional Object-Based Approach to Learning Physical Dynamics

1 code implementation1 Dec 2016 Michael B. Chang, Tomer Ullman, Antonio Torralba, Joshua B. Tenenbaum

By comparing to less structured architectures, we show that the NPE's compositional representation of the structure in physical interactions improves its ability to predict movement, generalize across variable object count and different scene configurations, and infer latent properties of objects such as mass.

The Emergence of Organizing Structure in Conceptual Representation

1 code implementation28 Nov 2016 Brenden M. Lake, Neil D. Lawrence, Joshua B. Tenenbaum

While this approach can learn intuitive organizations, including a tree for animals and a ring for the color circle, it assumes a strong inductive bias that considers only these particular forms, and each form is explicitly provided as initial knowledge.

Human collective intelligence as distributed Bayesian inference

no code implementations5 Aug 2016 Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland

To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.

Bayesian Inference Decision Making

Single Image 3D Interpreter Network

no code implementations29 Apr 2016 Jiajun Wu, Tianfan Xue, Joseph J. Lim, Yuandong Tian, Joshua B. Tenenbaum, Antonio Torralba, William T. Freeman

In this work, we propose 3D INterpreter Network (3D-INN), an end-to-end framework which sequentially estimates 2D keypoint heatmaps and 3D object structure, trained on both real 2D-annotated images and synthetic 3D data.

Image Retrieval

Building Machines That Learn and Think Like People

no code implementations1 Apr 2016 Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, Samuel J. Gershman

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people.

Board Games Object Recognition

Understanding Visual Concepts with Continuation Learning

no code implementations22 Feb 2016 William F. Whitney, Michael Chang, tejas kulkarni, Joshua B. Tenenbaum

We introduce a neural network architecture and a learning algorithm to produce factorized symbolic representations.

Atari Games Frame

Modeling Human Ad Hoc Coordination

1 code implementation11 Feb 2016 Peter M. Krafft, Chris L. Baker, Alex Pentland, Joshua B. Tenenbaum

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action.

Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model

no code implementations3 Dec 2015 Ryo Nakahashi, Chris L. Baker, Joshua B. Tenenbaum

Here we model how humans infer subgoals from observations of complex action sequences using a nonparametric Bayesian model, which assumes that observed actions are generated by approximately rational planning over unknown subgoal sequences.

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

1 code implementation3 Dec 2015 Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum

CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view.

Bayesian Inference Common Sense Reasoning

Picture: A Probabilistic Programming Language for Scene Perception

no code implementations CVPR 2015 Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka

Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision.

3D Human Pose Estimation 3D Object Reconstruction +2

Risk and Regret of Hierarchical Bayesian Learners

no code implementations19 May 2015 Jonathan H. Huggins, Joshua B. Tenenbaum

Common statistical practice has shown that the full power of Bayesian methods is not realized until hierarchical priors are used, as these allow for greater "robustness" and the ability to "share statistical strength."

Deep Convolutional Inverse Graphics Network

1 code implementation NeurIPS 2015 Tejas D. Kulkarni, Will Whitney, Pushmeet Kohli, Joshua B. Tenenbaum

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that learns an interpretable representation of images.

Inverse Graphics with Probabilistic CAD Models

no code implementations4 Jul 2014 Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum

We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs.

3D Human Pose Estimation

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs

no code implementations NeurIPS 2013 Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum

The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement.

Probabilistic Programming

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

4 code implementations20 Feb 2013 David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani

Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art.

Time Series

Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence

no code implementations19 Dec 2012 Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum

In the intervening years, the idea of cognition as computation has emerged as a fundamental tenet of Artificial Intelligence (AI) and cognitive science.

Common Sense Reasoning

Church: a language for generative models

no code implementations13 Jun 2012 Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum

We introduce Church, a universal language for describing stochastic generative processes.

Learning to Learn with Compound HD Models

no code implementations NeurIPS 2011 Antonio Torralba, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov

We introduce HD (or ``Hierarchical-Deep'') models, a new compositional learning architecture that integrates deep learning models with structured hierarchical Bayesian models.

Object Recognition

Dynamic Infinite Relational Model for Time-varying Relational Data Analysis

no code implementations NeurIPS 2010 Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua B. Tenenbaum

We propose a new probabilistic model for analyzing dynamic evolutions of relational data, such as additions, deletions and split & merge, of relation clusters like communities in social networks.

Nonparametric Bayesian Policy Priors for Reinforcement Learning

no code implementations NeurIPS 2010 Finale Doshi-Velez, David Wingate, Nicholas Roy, Joshua B. Tenenbaum

We consider reinforcement learning in partially observable domains where the agent can query an expert for demonstrations.

reinforcement-learning

Perceptual Multistability as Markov Chain Monte Carlo Inference

no code implementations NeurIPS 2009 Samuel Gershman, Ed Vul, Joshua B. Tenenbaum

While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian inference algorithmically.

Bayesian Inference

Modelling Relational Data using Bayesian Clustered Tensor Factorization

no code implementations NeurIPS 2009 Ilya Sutskever, Joshua B. Tenenbaum, Ruslan R. Salakhutdinov

We consider the problem of learning probabilistic models for complex relational structures between various types of objects.

Cannot find the paper you are looking for? You can Submit a new open access paper.