20 citations · 72 across the 13 of their papers we have counts for
15 papers
Theories of Mind as Domain-Specific Languages of Thought
Kartik Chandra, Jonathan Ragan-Kelley, Joshua B. Tenenbaum +1
What kind of thing is a ``theory of mind''? We propose to formalize theories of mind as domain-specialized programming languages, which can be used to reason about the mental state…
Meschers: Geometry Processing of Impossible Objects
Ana Dodik, Isabella Yu, Kartik Chandra +4
Impossible objects, geometric constructions that humans can perceive but that cannot exist in real life, have been a topic of intrigue in visual arts, perception, and graphics, yet…
Sycophantic Chatbots Cause Delusional Spiraling, Even in Ideal Bayesians
Kartik Chandra, Max Kleiman-Weiner, Jonathan Ragan-Kelley +1
"AI psychosis" or "delusional spiraling" is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot convers…
Empathy in Explanation
Katherine M. Collins, Kartik Chandra, Adrian Weller +2
Why do we give the explanations we do? Recent work has suggested that we should think of explanation as a kind of cooperative social interaction, between a why-question-asker and a…
Sketching With Your Voice: "Non-Phonorealistic" Rendering of Sounds via Vocal Imitation
Matthew Caren, Kartik Chandra, Joshua B. Tenenbaum +2
We present a method for automatically producing human-like vocal imitations of sounds: the equivalent of "sketching," but for auditory rather than visual representation. Starting w…
Building Machines that Learn and Think with People
Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt +10
What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and t…
WatChat: Explaining perplexing programs by debugging mental models
Kartik Chandra, Katherine M. Collins, Will Crichton +6
Often, a good explanation for a program's unexpected behavior is a bug in the programmer's code. But sometimes, an even better explanation is a bug in the programmer's mental model…
How to guess a gradient
Utkarsh Singhal, Brian Cheung, Kartik Chandra +4
How much can you say about the gradient of a neural network without computing a loss or knowing the label? This may sound like a strange question: surely the answer is "very little…
Differentiating Metropolis-Hastings to Optimize Intractable Densities
Gaurav Arya, Ruben Seyer, Frank Schäfer +7
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete…
Acting as Inverse Inverse Planning
Kartik Chandra, Tzu-Mao Li, Josh Tenenbaum +1
Great storytellers know how to take us on a journey. They direct characters to act -- not necessarily in the most rational way -- but rather in a way that leads to interesting situ…
Inferring the Future by Imagining the Past
Kartik Chandra, Tony Chen, Tzu-Mao Li +2
A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they mi…
Designing Perceptual Puzzles by Differentiating Probabilistic Programs
Kartik Chandra, Tzu-Mao Li, Joshua Tenenbaum +1
We design new visual illusions by finding "adversarial examples" for principled models of human perception -- specifically, for probabilistic models, which treat vision as Bayesian…
Gradient Descent: The Ultimate Optimizer
Kartik Chandra, Audrey Xie, Jonathan Ragan-Kelley +1
Working with any gradient-based machine learning algorithm involves the tedious task of tuning the optimizer's hyperparameters, such as its step size. Recent work has shown how the…
SPoC: Search-based Pseudocode to Code
Sumith Kulal, Panupong Pasupat, Kartik Chandra +4
We consider the task of mapping pseudocode to long programs that are functionally correct. Given test cases as a mechanism to validate programs, we search over the space of possibl…
Bonsai: Synthesis-Based Reasoning for Type Systems
Kartik Chandra, Rastislav Bodik
We describe algorithms for symbolic reasoning about executable models of type systems, supporting three queries intended for designers of type systems. First, we check for type sou…