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How do people perform general-purpose physical reasoning across a variety of scenarios in everyday life? Across two stud ies with seven different physical scenarios, we asked participants to predict whether or where two objects will make contact. People achieved high accuracy and were highly consistent with each other in their predictions. We hypothesize that this robust generalization is a consequence of mental simulations of noisy physics. We designed an “intuitive physics engine” model to capture this generalizable simulation. We find that this model generalized in human-like ways to unseen stimuli and to a different query of predictions. We evaluated several state-of-the-art deep learning and scene feature models on the same task and found that they could not explain human predictions as well. This study provides evidence that human’s robust generalization in physics predictions are supported by a probabilistic simulation model, and suggests the need for structure in learned dynamics models. 

Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand. Decades of hierarchical planning techniques have used domain-specific temporal action abstractions to support efficient and accurate planning, almost always relying on human priors and domain knowledge to decompose hard tasks into smaller subproblems appropriate for a goal or set of goals. This paper describes Ada (Action Domain Acquisition), a framework for automatically constructing task-specific planning representations using task-general background knowledge from language models (LMs). Starting with a general-purpose hierarchical planner and a low-level goal-conditioned policy, Ada interactively learns a library of planner-compatible high-level action abstractions and low-level controllers adapted to a particular domain of planning tasks. On two language-guided interactive planning benchmarks (Mini Minecraft and ALFRED Household Tasks), Ada strongly outperforms other approaches that use LMs for sequential decision- making, offering more accurate plans and better generalization to complex tasks. 

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.” However, in this paper, we show that gradients are more structured than previously thought. Gradients lie in a predictable low-dimensional subspace which depends on the network architecture and incoming features. Exploiting this structure can significantly improve gradient-free optimization schemes based on directional derivatives, which have struggled to scale beyond small networks trained on toy datasets. We study how to narrow the gap in optimization performance between methods that calculate exact gradients and those that use directional derivatives. Furthermore, we highlight new challenges in overcoming the large gap between optimizing with exact gradients and guessing the gradients. 

Denoising diffusion models have emerged as a powerful class of generative models capable of capturing the distributions of complex, real-world signals. However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks. In inverse graphics, for instance, we seek to sample from a distribution over 3D scenes consistent with an image but do not have access to ground-truth 3D scenes, only 2D images. We present a new class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never observed directly, but instead are only measured through a known differentiable forward model that generates partial observations of the unknown signal. To accomplish this, we directly integrate the forward model into the denoising process. At test time, our approach enables us to sample from the distribution over underlying signals consistent with some partial observation. We demonstrate the efficacy of our approach on three challenging computer vision tasks. For instance, in inverse graphics, we demonstrate that our model enables us to directly sample from the distribution 3D scenes consistent with a single 2D input image. 

Denoising diffusion models have emerged as a powerful class of generative models capable of capturing the distributions of complex, real-world signals. However, current approaches can only model distributions for which training samples are directly accessible, which is not the case in many real-world tasks. In inverse graphics, for instance, we seek to sample from a distribution over 3D scenes consistent with an image but do not have access to ground-truth 3D scenes, only 2D images. We present a new class of denoising diffusion probabilistic models that learn to sample from distributions of signals that are never observed directly, but instead are only measured through a known differentiable forward model that generates partial observations of the unknown signal. To accomplish this, we directly integrate the forward model into the denoising process. At test time, our approach enables us to sample from the distribution over underlying signals consistent with some partial observation. We demonstrate the efficacy of our approach on three challenging computer vision tasks. For instance, in inverse graphics, we demonstrate that our model enables us to directly sample from the distribution 3D scenes consistent with a single 2D input image. 

Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised discovery of object-centric neural scene representations. However, the high cost of ray-marching, exacerbated by the fact that each object representation has to be ray-marched separately, leads to insufficiently sampled radiance fields and thus, noisy renderings, poor framerates, and high memory and time complexity during training and rendering. Here, we propose to represent objects in an object-centric, compositional scene representation as light fields. We propose a novel light field compositor module that enables reconstructing the global light field from a set of object-centric light fields. Dubbed Compositional Object Light Fields (COLF), our method enables unsupervised learning of object-centric neural scene representations, state-of-the-art reconstruction and novel view synthesis performance on standard datasets, and rendering and training speeds at orders of magnitude faster than existing 3D approaches. 

We present a new algorithm that synthesizes functional reactive programs from observation data. The key novelty is to iterate between a functional synthesis step, which attempts to generate a transition function over observed states, and an automata synthesis step, which adds any additional latent state necessary to fully account for the observations. We develop a functional reactive DSL called Autumn that can express a rich variety of causal dynamics in time-varying, Atari-style grid worlds, and apply our method to synthesize Autumn programs from data. We evaluate our algorithm on a benchmark suite of 30 Autumn programs as well as a third-party corpus of grid-world-style video games. We find that our algorithm synthesizes 27 out of 30 programs in our benchmark suite and 21 out of 27 programs from the third-party corpus, including several programs describing complex latent state transformations, and from input traces containing hundreds of observations. We expect that our approach will provide a template for how to integrate functional and automata synthesis in other induction domains. 

Abstract Automated, data-driven construction and evaluation of scientific models and theories is a long-standing challenge in artificial intelligence. We present a framework for algorithmically synthesizing models of a basic part of human language: morpho-phonology, the system that builds word forms from sounds. We integrate Bayesian inference with program synthesis and representations inspired by linguistic theory and cognitive models of learning and discovery. Across 70 datasets from 58 diverse languages, our system synthesizes human-interpretable models for core aspects of each language’s morpho-phonology, sometimes approaching models posited by human linguists. Joint inference across all 70 data sets automatically synthesizes a meta-model encoding interpretable cross-language typological tendencies. Finally, the same algorithm captures few-shot learning dynamics, acquiring new morphophonological rules from just one or a few examples. These results suggest routes to more powerful machine-enabled discovery of interpretable models in linguistics and other scientific domains. 

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 specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping.