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Found 23 papers, 12 papers with code
DiffVL: Scaling Up Soft Body Manipulation using Vision-Language Driven Differentiable Physics
Combining gradient-based trajectory optimization with differentiable physics simulation is an efficient technique for solving soft-body manipulation problems.
Generating Pragmatic Examples to Train Neural Program Synthesizers
Programming-by-example is the task of synthesizing a program that is consistent with a set of user-provided input-output examples.
Learning a Hierarchical Planner from Humans in Multiple Generations
Compared to learning from demonstrations or experiences, programmatic learning allows the machine to acquire a novel skill as soon as the program is written, and, by building a library of programs, a machine can quickly learn how to perform complex tasks.
Hypothesis Search: Inductive Reasoning with Language Models
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.
Amortizing Pragmatic Program Synthesis with Rankings
We prove that for a pragmatic synthesizer that uses a single demonstration, our global ranking method exactly replicates RSA's ranked responses.
ANPL: Towards Natural Programming with Interactive Decomposition
We deploy ANPL on the Abstraction and Reasoning Corpus (ARC), a set of unique tasks that are challenging for state-of-the-art AI systems, showing it outperforms baseline programming systems that (a) without the ability to decompose tasks interactively and (b) without the guarantee that the modules can be correctly composed together.
Ranked #4 on
Code Generation
on HumanEval
Text Editing as Imitation Game
Text editing, such as grammatical error correction, arises naturally from imperfect textual data.
Efficient Pragmatic Program Synthesis with Informative Specifications
Surprisingly, we find that the synthesizer assuming a factored approximation performs better than a synthesizer assuming an exact joint distribution when evaluated on natural human inputs.
Communicating Natural Programs to Humans and Machines
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.
Engineering Sketch Generation for Computer-Aided Design
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects.
Program Synthesis Guided Reinforcement Learning for Partially Observed Environments
Our results demonstrate that our approach can obtain the benefits of program-guided reinforcement learning without requiring the user to provide a new guiding program for every new task.
Neurosymbolic Transformers for Multi-Agent Communication
We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication.
Representing Partial Programs with Blended Abstract Semantics
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.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software.
Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Reconstruction
We provide a dataset of 8, 625 designs, comprising sequential sketch and extrude modeling operations, together with a complementary environment called the Fusion 360 Gym, to assist with performing CAD reconstruction.
Program Synthesis with Pragmatic Communication
Given a specification, we score a candidate program both on its consistency with the specification, and also whether a rational speaker would chose this particular specification to communicate that program.
Compiler Auto-Vectorization with Imitation Learning
We show that the learnt policy produces a vectorization scheme which is better than industry standard compiler heuristics both in terms of static measures and runtime performance.
Write, Execute, Assess: Program Synthesis with a REPL
We present a neural program synthesis approach integrating components which write, execute, and assess code to navigate the search space of possible programs.
Verifiable Reinforcement Learning via Policy Extraction
While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies.
Learning to select examples for program synthesis
In this paper we address this challenge by constructing a representative subset of examples that is both small and is able to constrain the solver sufficiently.
Selecting Representative Examples for Program Synthesis
Program synthesis is a class of regression problems where one seeks a solution, in the form of a source-code program, mapping the inputs to their corresponding outputs exactly.
Learning to Acquire Information
Finding the optimal subset of observations is intractable in general, thus we focus on the problem of active diagnosis, where the agent selects the next most-informative observation based on the results of previous observations.
sk_p: a neural program corrector for MOOCs
We present a novel technique for automatic program correction in MOOCs, capable of fixing both syntactic and semantic errors without manual, problem specific correction strategies.
