Search Results for author: Nick Haber

Found 24 papers, 7 papers with code

Active World Model Learning in Agent-rich Environments with Progress Curiosity

no code implementations ICML 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

World models are a family of predictive models that solve self-supervised problems on how the world evolves.

Generating and Evaluating Tests for K-12 Students with Language Model Simulations: A Case Study on Sentence Reading Efficiency

no code implementations10 Oct 2023 Eric Zelikman, Wanjing Anya Ma, Jasmine E. Tran, Diyi Yang, Jason D. Yeatman, Nick Haber

Developing an educational test can be expensive and time-consuming, as each item must be written by experts and then evaluated by collecting hundreds of student responses.

Language Modelling Sentence

ContextRef: Evaluating Referenceless Metrics For Image Description Generation

1 code implementation21 Sep 2023 Elisa Kreiss, Eric Zelikman, Christopher Potts, Nick Haber

None of the methods is successful with ContextRef, but we show that careful fine-tuning yields substantial improvements.

Hypothesis Search: Inductive Reasoning with Language Models

no code implementations11 Sep 2023 Ruocheng Wang, Eric Zelikman, Gabriel Poesia, Yewen Pu, Nick Haber, Noah D. Goodman

Because of the prohibitive cost of generation with state-of-the-art LLMs, we consider a middle step to filter the set of hypotheses that will be implemented into programs: we either ask the LLM to summarize into a smaller set of hypotheses, or ask human annotators to select a subset of the hypotheses.

In-Context Learning

Developmental Curiosity and Social Interaction in Virtual Agents

no code implementations22 May 2023 Chris Doyle, Sarah Shader, Michelle Lau, Megumi Sano, Daniel L. K. Yamins, Nick Haber

We also find that learning a world model in the presence of an attentive caregiver helps the infant agent learn how to predict scenarios with challenging social and physical dynamics.

Measuring and Modeling Physical Intrinsic Motivation

no code implementations22 May 2023 Julio Martinez, Felix Binder, Haoliang Wang, Nick Haber, Judith Fan, Daniel L. K. Yamins

Finally, linearly combining the adversarial model with the number of collisions in a scene leads to the greatest improvement in predictivity of human responses, suggesting humans are driven towards scenarios that result in high information gain and physical activity.

Partial-View Object View Synthesis via Filtered Inversion

no code implementations3 Apr 2023 Fan-Yun Sun, Jonathan Tremblay, Valts Blukis, Kevin Lin, Danfei Xu, Boris Ivanovic, Peter Karkus, Stan Birchfield, Dieter Fox, Ruohan Zhang, Yunzhu Li, Jiajun Wu, Marco Pavone, Nick Haber

At inference, given one or more views of a novel real-world object, FINV first finds a set of latent codes for the object by inverting the generative model from multiple initial seeds.

Object

Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions

1 code implementation20 Dec 2022 Eric Zelikman, Qian Huang, Gabriel Poesia, Noah D. Goodman, Nick Haber

Despite recent success in large language model (LLM) reasoning, LLMs struggle with hierarchical multi-step reasoning tasks like generating complex programs.

Automated Theorem Proving Code Generation +4

Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images

no code implementations26 Jan 2022 Peter Washington, Cezmi Onur Mutlu, Aaron Kline, Kelley Paskov, Nate Tyler Stockham, Brianna Chrisman, Nick Deveau, Mourya Surhabi, Nick Haber, Dennis P. Wall

Computer Vision (CV) classifiers which distinguish and detect nonverbal social human behavior and mental state can aid digital diagnostics and therapeutics for psychiatry and the behavioral sciences.

Active Learning BIG-bench Machine Learning +4

Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels

no code implementations10 Jan 2021 Peter Washington, Onur Cezmi Mutlu, Emilie Leblanc, Aaron Kline, Cathy Hou, Brianna Chrisman, Nate Stockham, Kelley Paskov, Catalin Voss, Nick Haber, Dennis Wall

While the F1-score for a one-hot encoded classifier is much higher (94. 33% vs. 78. 68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t=3. 2827, p=0. 0014).

BIG-bench Machine Learning

Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders

no code implementations16 Dec 2020 Peter Washington, Haik Kalantarian, Jack Kent, Arman Husic, Aaron Kline, Emilie Leblanc, Cathy Hou, Cezmi Mutlu, Kaitlyn Dunlap, Yordan Penev, Maya Varma, Nate Stockham, Brianna Chrisman, Kelley Paskov, Min Woo Sun, Jae-Yoon Jung, Catalin Voss, Nick Haber, Dennis P. Wall

The classifier achieved 66. 9% balanced accuracy and 67. 4% F1-score on the entirety of CAFE as well as 79. 1% balanced accuracy and 78. 0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels.

Emotion Classification

Active World Model Learning with Progress Curiosity

no code implementations15 Jul 2020 Kuno Kim, Megumi Sano, Julian De Freitas, Nick Haber, Daniel Yamins

Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.

A Wearable Social Interaction Aid for Children with Autism

no code implementations19 Apr 2020 Nick Haber, Catalin Voss, Jena Daniels, Peter Washington, Azar Fazel, Aaron Kline, Titas De, Terry Winograd, Carl Feinstein, Dennis P. Wall

With most recent estimates giving an incidence rate of 1 in 68 children in the United States, the autism spectrum disorder (ASD) is a growing public health crisis.

Emotion Recognition Memorization

Learning to Play With Intrinsically-Motivated, Self-Aware Agents

no code implementations NeurIPS 2018 Nick Haber, Damian Mrowca, Stephanie Wang, Li F. Fei-Fei, Daniel L. Yamins

We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.

motion prediction Object

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.

Relation Network

Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation

no code implementations21 Feb 2018 Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins

Moreover, the world model that the agent learns supports improved performance on object dynamics prediction and localization tasks.

motion prediction Object

Learning to Play with Intrinsically-Motivated Self-Aware Agents

no code implementations21 Feb 2018 Nick Haber, Damian Mrowca, Li Fei-Fei, Daniel L. K. Yamins

We demonstrate that this policy causes the agent to explore novel and informative interactions with its environment, leading to the generation of a spectrum of complex behaviors, including ego-motion prediction, object attention, and object gathering.

motion prediction Object

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