SLM models code syntax as trees to generate any programming language code.
problem Generating any piece of code in a given language without restrictions.
method Structural language modeling (SLM) decomposes code into ASTs and estimates probabilities over nodes.
result SLM model generates arbitrary code in any language, outperforming previous methods.
Develops deep learning for logical code segmentation.
problem Lack of logically segmented source code.
method Novel deep learning approach to generate logical code segments.
result Improves software analysis tasks like commenting, bug detection, and code synthesis.
Neural networks model code edits from source code changes.
problem Modeling dynamic changes in source code.
method Developed neural networks to learn and predict code edits.
result Attentional and pointer network components provide best performance.
Improved source code summarization using extended Tree-LSTM.
problem Challenges in applying LSTM to structured source code.
method Extended Tree-LSTM for abstract syntax trees (ASTs).
result Multi-way Tree-LSTM achieves better results than state-of-the-art techniques.
New quantum codes improve error correction with local tests.
problem Improving quantum error correction efficiency.
method Introducing hemicubic codes and exploiting their local testability.
result Quantum codes with improved local testability and error correction.
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.
CCA features from medical codes predict future surgeries.
problem Predicting future surgeries based on medical codes.
method Canonical correlation analysis applied to sequences of medical codes.
result CCA embeddings capture meaningful relationships among medical codes.
MTDS improves sequence generation adaptability via latent code control.
problem Lack of adaptability in sequence generation models like RNNs.
method Hierarchical multi-task dynamical systems (MTDS) with latent code control.
result MTDS enables style transfer, interpolation, and morphing in generated sequences.
STRATA generates code adversarial examples efficiently without gradients.
problem Generating adversarial examples for code that retains functional meaning.
method Uses token frequency statistics to construct gradient-free adversarial examples.
result Empirically outperforms gradient-based methods with less information and effort.
New technique for flow models achieves theoretical compression lengths.
problem No guaranteed computationally efficient codes for flow models.
method Local bits-back coding for flow models.
result Efficient algorithms achieve theoretical codelengths for flow models.
New model generates code from GUI screenshots using attention and hierarchical structure.
problem Challenges in converting abstract image features to code.
method Attention-based hierarchical decoder trained end-to-end.
result Model outperforms state-of-the-art methods on GUI-code datasets.
Generative AI decodes quantum codes without labeled data.
problem Efficient decoding of quantum error-correcting codes.
method Generative Transformers learn logical operators from unsupervised syndromes.
result Significantly better decoding accuracy than traditional methods.
A new CNN-based code generator outperforms RNNs by 5%.
problem Capturing long sequences in code generation using RNNs.
method Grammar-based structural CNN with tree-based and pre-order convolution modules.
result Significantly outperforms previous state-of-the-art methods by 5 percentage points.
Tangent uses Python code transformation for automatic differentiation.
problem Efficient and readable automatic differentiation in Python.
method Source code transformation to generate derivative code.
result Readable and debuggable gradient code in Python.
Generative model learns compact codes for video recovery.
problem Efficiently represent and reconstruct videos from missing data.
method Generative network trained to map compact latent codes to images, with low-rank and similarity constraints.
result Can recover true video sequences even if not in pretrained network's range.
CODE2SEQ generates natural language sequences from code snippets.
problem Generating natural language descriptions from code.
method CODE2SEQ represents code as AST paths and uses attention to select relevant paths.
result CODE2SEQ outperforms previous models for code-to-text tasks.
A new predictive coding algorithm improves machine learning performance.
problem Improving performance of variational inference algorithms in high-dimensional, structured problems.
method Divide-and-Conquer Predictive Coding (DCPC) that respects generative model correlation structure and performs maximum-likelihood updates.
result DCPC achieves better numerical performance and accurate inference in structured generative models.
CoNCRA uses CNN to find code snippets matching developer intent.
problem Finding relevant code snippets using general search engines.
method Convolutional Neural Network approach to code retrieval.
result Improved code retrieval by 5% on average, top 3 positions 80% of the time.
We use matricial free energy to regularize autoencoders, producing Gaussian-like codes.
problem Generating Gaussian-like codes for autoencoders.
method Define a differentiable loss function based on singular values of the code matrix, minimizing matricial free energy.
result Minimizing matricial free energy results in Gaussian-like codes that generalize.
Gradient coding improves distributed learning efficiency.
problem Mitigating straggler issues in distributed learning.
method Gradient coding using cyclic MDS codes and expander graphs.
result Approximate gradient codes enable faster convergence and less computation.
Khovanov homology helps create quantum error-correcting codes.
problem Creating robust quantum error-correcting codes.
method Using Khovanov homology and its extensions to define and analyze quantum codes.
result New families of quantum codes with desirable properties.
Transformer model improves source code summarization.
problem Generating readable summaries of source code.
method Transformer model with self-attention mechanism for code representation.
result Transformer model outperforms state-of-the-art techniques.
Flexible priors improve deep generative models.
problem Training deep generative models with complex deterministic models.
method Induce flexible code distributions directly from data.
result More powerful generative models, better latent structure modeling, explicit generalization control.
Contrastive Code Representation Learning improves code summarization and type inference.
problem Code representations are sensitive to edits, hindering downstream semantic understanding tasks.
method ContraCode: a contrastive pre-training task that learns code functionality.
result Contrastive pre-training improves code summarization and type inference accuracy.
Generative model uses graphs to create natural-sounding code.
problem Creating semantically meaningful source code with syntactic and semantic constraints.
method Graph representation for intermediate state, interleaves grammar-driven expansion with graph augmentation and neural message passing.
result Generative model outperforms baselines in generating natural-sounding code.
Gradient coding improves distributed learning by tolerating failures.
problem Mitigating stragglers in distributed learning.
method Carefully replicating data blocks and coding across gradients.
result Shows tolerance to failures and stragglers for Synchronous Gradient Descent.
Deepcode codes outperform traditional ones by 3 orders of magnitude.
problem Designing reliable codes for feedback channels with Gaussian noise.
method Integrating deep learning with information theory for novel codes.
result Deepcode codes outperform known codes by 3 orders of magnitude in reliability.
Unsupervised framework learns latent codes for controllable generation.
problem Challenging to achieve controllable generation with GANs.
method Self-training iterative feedback from discriminator to generator.
result Better disentanglement and semantic meaningful latent codes.
Abstract Syntax Networks generate code and parse text with high accuracy.
problem Mapping unstructured inputs to well-formed outputs for code generation and semantic parsing.
method Dynamic AST construction using a decoder with a modular structure.
result 79.2 BLEU and 22.7% exact match accuracy on Hearthstone dataset.
New model learns execution of code using GNNs.
problem Stagnation of computer system performance due to Moore's Law.
method Multi-task GNN over low-level code and program state.
result Improved performance on dynamic tasks (26% and 45% over state-of-the-art).
MISIM improves code similarity systems with neural learning.
problem Improving accuracy of code semantics similarity systems.
method Context-aware semantics structure and neural code similarity scoring algorithm.
result MISIM achieves 8.08% better accuracy than state-of-the-art systems.
Neural networks improve error correction in topological codes.
problem Finding optimal correction of errors in generic stabilizer codes is computationally hard.
method Systematic study of versatile neural-network decoders for topological codes.
result Neural decoders significantly improve error-correction threshold over leading efficient decoders.
New fault-tolerant quantum gates for homological LDPC codes with constant or almost-constant rate.
problem Fault-tolerant quantum computing for homological LDPC codes with constant or almost-constant encoding rate.
method Derive generic formula for transversal and logical gates acting on 3-manifolds, using higher symmetries and cup product cohomology.
result Parallelizable logical gates for homological LDPC codes with constant or almost-constant rate.
Unified framework for speaker-adaptive models using scaling and bias codes.
problem Improving speaker-adaptive neural-network based speech synthesis systems.
method Unified framework representation and generalized scaling and bias codes.
result Improves performance of speaker adaptation compared to conventional methods.
The paper extends lossy coding to nonlinear latent representations.
problem Learning finite-dimensional coding schemes with nonlinear reconstruction maps.
method Generalizes Maurer--Pontil framework to nonlinear maps, connects to generative modeling, and provides generalization bounds.
result Established a connection to approximate generative modeling and presented generalization bounds.
PCGs encompass a broader range of neural networks.
problem Understanding the broader scope of neural network models.
method Proving PCGs as a superset of feedforward neural networks.
result PCGs represent a wider class of neural network models.
This paper presents a general coding method where data in a Hilbert space are represented by finite dimensional coding vectors. The method is based on empirical risk minimization within a certain class of linear operators, which map the set of coding vectors to the Hilbert space. Two results bounding the expected recon…
The k-dimensional coding schemes refer to a collection of methods that attempt to represent data using a set of representative k-dimensional vectors, and include non-negative matrix factorization, dictionary learning, sparse coding, k-means clustering and vector quantization as special cases. Previous generalizat…
GKP codes connect quantum gates to algebraic curves, enabling fault-tolerant quantum computation.
problem Implementing fault-tolerant quantum computation in quantum harmonic oscillator systems.
method Exploring the topological and algebraic structure of GKP codes, showing how gates correspond to symplectic automorphisms and mapping class groups of surfaces.
result GKP Clifford gates are identified with symplectic automorphisms of GKP lattices and mapping class groups of surfaces, providing a topological interpretation of fault tolerance.
New method trains generative models by reversing generator maps.
problem Training deep neural network generators.
method Non-parametrically estimate flexible code distributions by reversing generator maps.
result More powerful generative models, better latent structure modeling, explicit generalization control.
Topological theory for qLDPC codes enables non-Clifford gates and magic state injection.
problem Fault-tolerant quantum computation in qLDPC codes with non-Clifford gates and magic state resources.
method Developed a topological theory using simplicial or CW complex structures and deformation retraction.
result Achieved non-Clifford gates and magic state injection in qLDPC codes with constant rate and polynomial distance.
Improves autoencoder reconstruction quality by approximating latent space with adversarial learning.
problem Ambiguity in autoencoder reconstructions and difficulty in matching true distribution.
method Adversarially Approximated Autoencoder (AAAE) using GAN for flexible latent space approximation.
result Generates more faithful reconstructions and maintains latent manifold structure.
New method learns sparse distributions by thresholding samples, improving performance and efficiency.
problem Sparse coding optimization in high-dimensional problems is computationally expensive and inefficient.
method Proposes a new variational sparse coding approach that learns sparse distributions by thresholding samples.
result Shows superior performance, statistical efficiency, and gradient estimation compared to other sparse distributions.
We study the problem of building generative models of natural source code (NSC); that is, source code written and understood by humans. Our primary contribution is to describe a family of generative models for NSC that have three key properties: First, they incorporate both sequential and hierarchical structure. Second…
Sparse codes improve optimal control tasks with correlated inputs.
problem Optimal control tasks with correlated feature inputs.
method Used a sparse code to represent natural images in an optimal control task solved with neuro-dynamic programming.
result An over-complete sparse code increases memory capacity and learning speed beyond a complete code.
Paper proposes cross-coding to improve conditional inference in VAEs.
problem Challenges in conditional inference with VAEs, especially for arbitrary queries.
method Cross-coding to approximate latent distributions after conditioning.
result Cross-coding variations outperform Hamiltonian Monte Carlo.
Deep sparse coding models resist adversarial examples.
problem Adversarial examples can fool deep learning models.
method Used deep sparse coding models to resist adversarial examples.
result Deep sparse coding models are robust to adversarial examples.
We study the concept of a code (or shift) space for a generalized iterated function system (GIFS in short). We prove that relations between GIFSs and their code spaces are analogous to the case of classical IFSs. As an application, we consider the problem of connectedness of attractors of GIFSs. Many of our results are…