no code implementations • 14 Feb 2024 • Yutaro Yamada, Khyathi Chandu, YuChen Lin, Jack Hessel, Ilker Yildirim, Yejin Choi
In this paper, we propose a language agent with chain-of-3D-thoughts (L3GO), an inference-time approach that can reason about part-based 3D mesh generation of unconventional objects that current data-driven diffusion models struggle with.
no code implementations • 11 Jan 2024 • Benjamin Peters, James J. DiCarlo, Todd Gureckis, Ralf Haefner, Leyla Isik, Joshua Tenenbaum, Talia Konkle, Thomas Naselaris, Kimberly Stachenfeld, Zenna Tavares, Doris Tsao, Ilker Yildirim, Nikolaus Kriegeskorte
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
1 code implementation • 23 Oct 2023 • Yutaro Yamada, Yihan Bao, Andrew K. Lampinen, Jungo Kasai, Ilker Yildirim
Large language models (LLMs) show remarkable capabilities across a variety of tasks.
no code implementations • 6 Oct 2023 • Ilker Yildirim, L. A. Paul
In what sense does a large language model have knowledge?
1 code implementation • 21 Feb 2023 • Qi Lin, Zifan Li, John Lafferty, Ilker Yildirim
Much of what we remember is not due to intentional selection, but simply a by-product of perceiving.
1 code implementation • 9 Jan 2023 • Ilker Yildirim, Max H. Siegel, Amir A. Soltani, Shraman Ray Chaudhari, Joshua B. Tenenbaum
Many surface cues support three-dimensional shape perception, but people can sometimes still see shape when these features are missing -- in extreme cases, even when an object is completely occluded, as when covered with a draped cloth.
no code implementations • 22 Dec 2022 • Yutaro Yamada, Yingtian Tang, Yoyo Zhang, Ilker Yildirim
Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval.
no code implementations • NeurIPS Workshop SVRHM 2021 • Yutaro Yamada, Yuval Kluger, Sahand Negahban, Ilker Yildirim
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
no code implementations • 29 Sep 2021 • Yutaro Yamada, Yuval Kluger, Sahand Negahban, Ilker Yildirim
To tackle the problem from a new perspective, we encourage closer collaboration between the robustness and 3D vision communities.
no code implementations • 11 May 2019 • Ilker Yildirim, Basil Saeed, Grace Bennett-Pierre, Tobias Gerstenberg, Joshua Tenenbaum, Hyowon Gweon
The ability to estimate task difficulty is critical for many real-world decisions such as setting appropriate goals for ourselves or appreciating others' accomplishments.
no code implementations • 5 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.
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.
no code implementations • 25 Jul 2017 • Ilker Yildirim, Tobias Gerstenberg, Basil Saeed, Marc Toussaint, Josh Tenenbaum
In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands.
no code implementations • NeurIPS 2015 • Jiajun Wu, Ilker Yildirim, Joseph J. Lim, Bill Freeman, Josh Tenenbaum
Humans demonstrate remarkable abilities to predict physical events in dynamic scenes, and to infer the physical properties of objects from static images.
no code implementations • 23 Sep 2014 • Ifeoma Nwogu, Goker Erdogan, Ilker Yildirim, Robert Jacobs
This paper presents a computational model of concept learning using Bayesian inference for a grammatically structured hypothesis space, and test the model on multisensory (visual and haptics) recognition of 3D objects.