Extends program induction for probabilistic programming.
problem Automatic probabilistic program synthesis for diverse data types.
method Further steps to extend previous work on program induction.
result Generalization over various data types (text, image, video).
This paper extends ABCD to discover time series structure using probabilistic program synthesis.
problem Discovering structure in time series data.
method Formulating ABCD in probabilistic program synthesis, using abstract syntax trees and probabilistic programs.
result Improved accuracy in time series clustering and interpolation/extrapolation.
SED integrates synthesis, execution, and debugging for neural program synthesis.
problem Challenges in synthesizing complex programs that match specifications.
method SED combines synthesis, execution, and debugging to improve neural program generation.
result SED reduces error rates and outperforms standard decoding methods.
Dataset of human-written problem statements and solutions for program synthesis.
problem Creating programs from natural language problem descriptions.
method Crowdsourced problem statements and solutions from programming competitions.
result Best model achieved 8.8% accuracy, indicating high complexity.
A neural program synthesis method with iterative fix operations.
problem Creating correct programs from input-output examples.
method Combines encoder-decoder synthesis with a differentiable fixer.
result Improves synthesis accuracy by reducing discrepancies between outputs and desired outputs.
CrossBeam learns to search more efficiently in program synthesis.
problem Efficiently searching through vast program spaces.
method Trains a neural model to guide program synthesis, combining previously explored programs.
result CrossBeam explores much smaller portions of the program space compared to state-of-the-art methods.
New approach generates better synthetic data for neural program synthesis.
problem Current approaches to neural program synthesis generalize poorly to real data.
method Adversarial approach to control synthetic data distributions.
result Proposed method outperforms current approaches.
New approach uses neural networks to learn program structure and parameters.
problem Learning programs and their structure efficiently.
method Free category prior over programs, end-to-end learning of structure and parameters.
result Neural networks can serve as primitives in probabilistic programs.
Improves neural program synthesis by addressing aliasing and syntax issues.
problem Ignoring program aliasing and syntax in neural program synthesis.
method Reinforcement learning and direct syntax maximization training.
result Improved accuracy, especially with limited training data.
MORL uses program synthesis to improve reinforcement learning policies.
problem Difficult to interpret and impose constraints on learned policies from black-box neural networks.
method Iterative framework combining program synthesis and behavior cloning.
result Programmatic representation allows for high-level modifications leading to improved learning.
EgoCoder synthesizes programs from text using neural networks.
problem Automatically generating programs to meet software developer demand.
method Hierarchical sequential neural network model to parse and synthesize programs.
result EgoCoder effectively captures hierarchical and sequential program patterns.
New methodology controls synthetic data bias for neural program synthesis.
problem Deep networks generalize poorly to certain data distributions when trained on synthetic examples.
method Proposes a new methodology to control and evaluate the bias of synthetic data distributions over programs and specifications.
result Training deep networks on controlled synthetic data distributions leads to improved cross-distribution generalization performance.
PATOIS synthesizes code from natural language using learned code idioms.
problem Synthesizing general-purpose source code from natural language specifications is challenging.
method PATOIS uses a neural synthesizer that interleaves high-level and low-level reasoning, incorporating learned code idioms from a corpus.
result Using learned code idioms improves the synthesizer's accuracy on semantic parsing datasets.
Program synthesis struggles with complex spatial relationships in image classification.
problem Challenges in solving Synthetic Visual Reasoning Test problems.
method Quantitative reanalysis of human and machine performance, improved program synthesis classifier, categorization of SVRT problems.
result Program synthesis is constrained by spatial relationships in images, not just shape specification.
System uses machine learning and automated reasoning to speed up PBE synthesis.
problem Slow synthesis in PBE due to domain-specific knowledge and large training datasets.
method Preprocess SyGuS PBE problems with a neural network to reduce search space, then use automated reasoning for faster solution.
result System outperforms all competing tools in the 2019 SyGuS Competition for the PBE Strings track by 47.65%.
Generative models learn complex spatial patterns using program synthesis.
problem Capturing complex global structure in data, especially in images.
method Incorporates programs representing global structure into generative models and learns these models through program synthesis.
result Significantly better at generating and completing images with global structure compared to state-of-the-art methods.
Seq2Seq models perform well in generating If-Then programs from natural language.
problem Creating If-Then programs for business process automation without technical expertise.
method Modeling If-Then programs as a sequence learning task using Seq2Seq approaches.
result Seq2Seq models can effectively generate If-Then programs from natural language.
HOUDINI learns algorithms across domains using program synthesis.
problem Lifelong learning of algorithmic tasks mixing perception and reasoning.
method Combining gradient descent with combinatorial search over programs.
result HOUDINI transfers high-level concepts more effectively than traditional methods.
BUSTLE synthesizes programs by learning from intermediate values.
problem Challenges in synthesizing complex programs due to large search space.
method Bottom-up search guided by a neural network trained on input-output examples.
result Bottom-up search with execution of intermediate programs provides valuable semantic information.
IReEn reveals functionality of black-box agents via iterative neural synthesis.
problem Revealing the functionality of a black-box agent without privileged information.
method Iterative refinement of candidate programs using neural program synthesis.
result The approach finds a functional equivalent program in 78% of cases, outperforming state-of-the-art.
Neural model guides PBE problem solving in programming.
problem Synthesizing programs from example inputs/outputs.
method Uses a neural model to guide miniKanren's constraint logic programming system.
result Synthesizes programs faster and generalizes to larger problems.
This paper evaluates how well neural models can solve complex tasks by breaking them into simpler ones.
problem Measuring neural models' ability to solve complex tasks by breaking them into simpler subtasks.
method Characterized axes of compositional generalization, introduced a benchmark suite of tasks, and improved Transformer models' attention mechanisms.
result Modified Transformer models generally perform better than natural baselines in solving complex tasks, but challenges remain.
PLANS synthesizes programs from noisy inputs using neural specs and filtering.
problem Synthesizing robust programs from noisy, raw inputs.
method Hybrid model combining neural extraction and rule-based synthesis with noise filtering.
result State-of-the-art performance in diverse environments with no ground-truth training.
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.
SAPS synthesizes code from natural language specifications.
problem Efficiently translating complex NL specifications into executable code.
method Structure-aware neural network using abstract syntax trees and LSTM.
result SAPS produces correct programs in over 92% of cases.
Stochastic programs simplify complex models with noise and nondeterminism.
problem Handling models with nuisance parameters, noise, and nondeterminism.
method Developed a reference implementation for stochastic probabilistic programs and inference.
result Efficient inference in models with noise and nondeterminism is possible.
Deployable probabilistic programming for Go and other languages.
problem Adding probabilistic programming to mainstream languages.
method Design guidelines and Infergo implementation for Go.
result Infergo demonstrates performance and applicability in various use cases.
Framework uses neural networks to learn fitness functions for machine programming.
problem Automatic software generation and crafting effective fitness functions.
method Genetic algorithms augmented with neural networks and a search heuristic.
result Framework discovers more correct programs with fewer candidate generations.
Generates long programs from inputs, optimizing multiple tasks.
problem Creating long programs from input-output pairs.
method Trains a neural network to map state and outputs to next program statement, optimizing multiple tasks concurrently.
result Creates programs twice as long as existing solutions, improving success rate and runtime.
Improves probabilistic programming by analyzing program structure.
problem Inefficiency and limitations of single inference algorithms in probabilistic programming.
method Three novel techniques: static and dynamic analyses to adapt programs for more efficient inference.
result Improves probabilistic programming by making inference more efficient.
Unsupervised algorithm parses CSG images into CFG without pretraining.
problem Sparse reward problem in unsupervised program synthesis for images.
method Grammar-encoded tree LSTM, entropy regularization, sampling without replacement.
result Recover meaningful programs in large search spaces (up to 3.8imes1028). Machine learning refactors knowledge to improve learning efficiency.
problem Inductive program synthesis efficiency through knowledge restructuring.
method Introduces Knorf, a system that refactors knowledge bases using constraint optimization.
result Learning from refactored knowledge improves predictive accuracy fourfold and reduces learning time by half.
Synthesizes static analysis for probabilistic programs.
problem Optimize learning process, verify models, improve programming interface.
method Organize and analyze static analysis techniques for probabilistic programming.
result Future directions for improvement in statistical machine learning.
Graph-based approach repairs programs from diagnostic feedback.
problem Learning to repair programs from limited labeled data and compiler error messages.
method Introduces program-feedback graph and graph neural network for reasoning, and self-supervised learning with unlabeled programs.
result DrRepair significantly outperforms prior work, achieving high repair rates.
SPoC uses search to translate pseudocode into correct programs with error localization.
problem Mapping pseudocode to functionally correct long programs.
method Search-based approach guided by compilation errors for credit assignment.
result Search improves synthesis success rate from 25.6% to 44.7%.
We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly struc…
Birch automates probabilistic modeling using a Turing-complete language.
problem Automating the matching of probabilistic models with inference methods.
method Formally describes models as programs, revealing structure and form dynamically.
result Probabilistic programming languages can tailor inference methods based on model structure and form.
The problem of replicating the flexibility of human common-sense reasoning has captured the imagination of computer scientists since the early days of Alan Turing's foundational work on computation and the philosophy of artificial intelligence. In the intervening years, the idea of cognition as computation has emerged …
We develop a technique for generalising from data in which models are samplers represented as program text. We establish encouraging empirical results that suggest that Markov chain Monte Carlo probabilistic programming inference techniques coupled with higher-order probabilistic programming languages are now sufficien…
High-quality image synthesis with diffusion models, achieving state-of-the-art FID score.
problem Generating high-quality images from latent variables.
method Training diffusion probabilistic models with a weighted variational bound, inspired by denoising score matching and Langevin dynamics.
result State-of-the-art FID score of 3.17 on CIFAR10 dataset.
Graduate-level introduction to probabilistic programming.
problem Designing and building probabilistic programming systems.
method First-order and higher-order probabilistic programming languages, inference algorithms, and gradient-based maximum likelihood estimation.
result Efficient inference methods and neural network parameterization.
Introduces Motion Programs for better video analysis of human motion.
problem Current video analysis focuses on raw pixels or keypoints, missing higher-level motion primitives.
method Introduces Motion Programs as a neuro-symbolic representation of motions as a composition of high-level primitives.
result Motion Programs accurately describe diverse human motions and improve downstream tasks.
Forward inference techniques such as sequential Monte Carlo and particle Markov chain Monte Carlo for probabilistic programming can be implemented in any programming language by creative use of standardized operating system functionality including processes, forking, mutexes, and shared memory. Exploiting this we have …
MultiVerse uses importance sampling for efficient causal reasoning in probabilistic programming.
problem Efficient causal reasoning in probabilistic models, especially counterfactual inference.
method Native implementation of importance sampling in probabilistic programming, optimizing inference through query structure.
result Significant optimisation of inference process through careful design choices and query structure consideration.
New method reduces infinite variance in probabilistic programs with rejection sampling.
problem Infinite variance in naive importance sampling for programs with rejection sampling.
method Developed a new amortized importance sampling estimator with finite variance proof.
result Empirically demonstrated efficiency and correctness compared to existing alternatives.
Pyro enables scalable AI models using probabilistic programming.
problem Developing complex probabilistic models for large datasets.
method Stochastic variational inference, PyTorch, Poutine.
result Pyro supports scalable AI models with high-dimensional data.
A new TTS method uses diffusion and VAE for better speech synthesis.
problem Improving text-to-speech synthesis for better speech quality and robustness.
method Combines diffusion probabilistic model and variational autoencoder for latent variable conversion.
result The method is robust to poor orthography and alignment errors.
Paper proposes using backpropagation for probabilistic program learning.
problem Difficult to learn probabilistic models from data.
method Learning parameters of a probabilistic program using backpropagation.
result Trains probabilistic models similar to neural networks.