A new stable edit distance for Reeb graphs is shown to be universal.
problem Stability and comparability of Reeb graphs under function similarity.
method Defined and proved stability and universality of Reeb graph edit distance.
result Reeb graph edit distance is the most stable and universal among distances.
MEGAN models chemical reactions as graph edits, improving synthesis planning.
problem Generating and predicting chemical reactions under constraints.
method End-to-end encoder-decoder neural model inspired by arrow pushing formalism.
result State-of-the-art accuracy in standard benchmarks for retrosynthesis prediction.
GSimCNN predicts graph similarity using CNNs, outperforming existing methods.
problem Challenging pairwise graph similarity computation due to NP-hardness.
method Graph Edit Distance (GED) as core metric, GSimCNN (Convolutional Neural Networks).
result State-of-the-art performance on graph similarity search.
Agents use object-oriented reasoning to solve problems more quickly.
problem Finding solutions to problems more efficiently.
method Hierarchical controller directs low-level agent to simulate alternate states of the world.
result Achieves similar reward levels as non-hierarchical agents but with better data efficiency.
funcGNN uses graph neural networks to estimate program similarity efficiently.
problem Estimating accurate program similarity for software engineering tasks.
method funcGNN trains on labeled CFG pairs to predict GED between unseen programs using effective embedding vectors.
result funcGNN achieves lower error rate (0.00194) and is 23 times faster than traditional methods.
New method for finding function correspondences in binary programs.
problem Finding a correspondence between functions of two binary programs.
method Formulated as a network alignment problem and solved using belief propagation.
result QBinDiff prototype outperforms state-of-the-art diffing tools.
Linear-time algorithm for optimal assignment in graph matching.
problem Finding optimal assignments between graph vertices efficiently.
method Developed an algorithm for linear-time optimal assignment using tree distances.
result Approximated edit distance between graphs in linear time.
CoSimGNN improves graph similarity computation for large graphs.
problem Efficiently computing graph similarity scores for large graphs.
method Embedding-coarsening-matching framework with adaptive pooling and fine-grained interactions.
result CoSimGNN achieves best performance in graph similarity computation.
Models learn to represent edits from natural language and code.
problem Learning distributed representations of edits.
method Combining a neural editor with an edit encoder.
result Models can capture the structure and semantics of edits.
PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.
problem Efficiently compute graph similarity scores for large graphs.
method Graph partitioning followed by subgraph-level and node-level comparisons using a graph neural network.
result PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks.
GraphSim computes graph similarity by matching node embeddings, outperforming existing methods.
problem Efficiently computing graph similarity between graphs of varying sizes and structures.
method GraphSim directly matches sets of node embeddings without fixed-dimensional graph representations.
result GraphSim achieves state-of-the-art performance on multiple real-world datasets.
ED-NeRF efficiently edits 3D scenes using latent space NeRF and improved loss functions.
problem Slow training speeds and inadequate editing loss functions in existing NeRF editing techniques.
method Embedding real-world scenes into latent space of LDM, using a unique refinement layer and an improved loss function.
result ED-NeRF achieves faster editing speed and improved output quality compared to state-of-the-art models.
Predicts edit survival in peer-production systems.
problem Predicting the survival of edits in large-scale peer-production systems.
method Modeling edit survival as a game between editor skill, component difficulty, and user-component interaction.
result Model significantly outperforms user reputation-based methods and specialized predictors.
Neural network for subgraph similarity computation with pruning.
problem Computing subgraph similarity between a target and query graph.
method Convert pruning to node relabeling, relax to differentiable problem, design neural network for SED computation.
result Establishes new state-of-the-art results across multiple benchmark datasets.
We present a new, efficient method for automatically detecting severe conflicts `edit wars' in Wikipedia and evaluate this method on six different language WPs. We discuss how the number of edits, reverts, the length of discussions, the burstiness of edits and reverts deviate in such pages from those following the gene…
A new smooth edit distance for easier optimization in machine learning.
problem Hard optimization of edit distance for variable-length sequences.
method Soft edit distance (SED) as a differentiable approximation.
result SED can be optimized with gradient methods and used for clustering.
Prototype sentences edited for better language models and quality.
problem Improving sentence generation quality and efficiency.
method Samples a prototype sentence, edits it, and uses a latent edit vector.
result Improves perplexity and generates higher quality sentences.
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.
SimGNN uses neural networks to quickly find similar graphs.
problem Efficiently computing graph similarity, especially for large graphs.
method Embedding function + attention mechanism + pairwise node comparison.
result SimGNN achieves better performance and faster computation than existing methods.
Ground-A-Video edits videos without training, preserving intended changes.
problem Complex multi-attribute video editing with omitted or wrong changes.
method Grounding-guided video-to-video translation with Cross-Frame Gated Attention.
result Zero-shot multi-attribute video editing with improved accuracy and frame consistency.
A framework that edits existing examples to generate structured outputs.
problem Generating complex outputs like code is hard.
method Retrieve a training example, edit it, and use a computationally efficient retrieval model.
result Retrieve-and-edit framework significantly boosts performance on code and card tasks.
A new method predicts organic reactions faster and more accurately.
problem Predicting reaction outcomes in complex molecules is computationally challenging.
method Identifies reaction centers, enumerates candidate products, and scores them using a Weisfeiler-Lehman Difference Network.
result Framework outperforms template-based methods with a 10% margin and runs faster.
Paper fine-tunes LLMs using user edits, unifying preference, supervision, and reward feedback.
problem Adapting LLMs to user preferences and feedback types.
method Derives bounds for learning algorithms from user edits, proposes an ensembling procedure.
result Ensembling procedure outperforms individual feedback methods and robustly adapts to different user-edit distributions.
Improves generative models by optimizing rewards and sample editing.
problem Efficiently generating high-reward samples with structural constraints.
method Introduces MDM-VGB, a discrete diffusion sampler that augments unmasking generation with reward-guided remasking.
result MDM-VGB achieves quadratic complexity and robustness to noise, outperforming heuristics like best-of-N. We define a pseudo-inverse for line graphs using linear integer programming.
problem Not all graphs have a corresponding root graph, making the line graph operation non-invertible.
method Propose a linear integer program to edit the smallest number of edges in the line graph to recover a root graph.
result The pseudo-inverse operation is well-behaved and works in practice as shown by empirical experiments.
We compute an approximate Fréchet mean for sets of sparse graphs.
problem Characterizing the location of a set of graphs in a metric space.
method We use the pseudometric defined by the ℓ₂ norm of eigenvalues of adjacency matrices.
result We describe an algorithm to approximate the Fréchet mean of a set of graphs.
AttGAN edits facial attributes by changing only what you want, preserving details.
problem Facial attribute editing with preservation of details.
method Encoder-decoder architecture with attribute classification and reconstruction learning.
result Outperforms state-of-the-arts on realistic attribute editing with preserved details.
New method uses coupled SDEs to edit images with high fidelity and consistency.
problem Challenges in editing image content with text-to-image models.
method Using coupled stochastic differential equations to guide generative model sampling.
result Achieves high prompt fidelity and near-pixel-level consistency.
RS-Del provides robustness for sequence classifiers against edit distance attacks.
problem Certifying robustness of discrete sequence classifiers against edit distance attacks.
method Randomized deletion (RS-Del) for discrete sequence classifiers, focusing on edit distance-bounded adversaries.
result Achieved a certified accuracy of 91% at an edit distance radius of 128 bytes on malware detection.
Paper proposes efficient image inversion and editing using rectified stochastic differential equations.
problem Inversion and editing of real images using generative models.
method Proposes RF inversion using dynamic optimal control and a linear quadratic regulator, extending to stochastic sampler for Flux.
result Allows state-of-the-art performance in zero-shot inversion and editing, outperforming prior works.
New method detects watermarks in LLM-generated text with human edits.
problem Dilution of watermark signals by human edits on LLM-generated text.
method Truncated goodness-of-fit test (Tr-GoF) for robust detection.
result Tr-GoF achieves optimality in robust detection of Gumbel-max watermark.
Adversarial edit attacks improve machine learning model security for tree data.
problem Improving security of machine learning models for tree-structured data.
method Extends adversarial attacks to tree-structured data using tree edit distance and black-box queries.
result Many tree classifiers can be effectively attacked, demonstrating the vulnerability of these models.
A technique to quickly fix mistakes in neural networks.
problem Fixing model errors in neural networks quickly and without affecting other samples.
method Editable Training, a model-agnostic training technique.
result Effectiveness demonstrated on large-scale image classification and machine translation tasks.
Machine learning experiments show IID assumption is flawed for bathymetry editing.
problem Flawed IID assumption in machine learning for bathymetry editing.
method Real-world computer-assisted labeling task, IID assumption analysis.
result Common random split leads to poor performance in machine learning.
The paper computes an approximation to the sample Frechet mean of graph sets using spectral information.
problem Characterizing the location of a set of graphs in a metric space.
method The Frechet mean is computed for sets of large graphs using the pseudometric defined by the norm between eigenvalues of adjacency matrices.
result An algorithm to approximate the sample Frechet mean of undirected unweighted graphs is described.
Metric learning improves tree edit distance for better classification.
problem Improving tree edit distance for better classification accuracy.
method Adaptive symbol embeddings to learn tree edit distance indirectly.
result Embedding edit distance learning (BEDL) improves upon state-of-the-art on multiple tree data sets.
GMED edits stored examples to improve continual learning.
problem Catastrophic forgetting in task-free continual learning.
method Gradient-based memory editing of stored examples.
result GMED-edited examples help prevent forgetting.
New seq2seq model can copy entire spans, outperforming simpler models in editing tasks.
problem Editing documents or source code using seq2seq models with explicit token copying.
method Extended seq2seq model capable of copying entire input spans to output in one step, new training and inference methods.
result New model consistently outperforms simpler baselines in editing tasks of natural language and source code.
Professional-grade software applications are powerful but complicated−expert users can achieve impressive results, but novices often struggle to complete even basic tasks. Photo editing is a prime example: after loading a photo, the user is confronted with an array of cryptic sliders like "clarity", "temp", and "high…
New metric learning approach for tree data reduces computation cost.
problem Efficiently computing distances between ordered labeled trees.
method Introduced pq-grams and a differentiable weighted pq-gram distance, combined with LMNN for optimization.
result Significantly reduces computation time for tree classification problems.
XL-Editor improves sentence post-editing using XLNet's variable-length insertion probability.
problem Post-editing sentences to refine generated text.
method XL-Editor trains XLNet to estimate variable-length insertion probabilities and apply post-editing operations.
result XL-Editor outperforms XLNet on text insertion and deletion tasks, and achieves significant style transfer improvements.
Paper introduces a graph-based approach for retrosynthesis prediction.
problem Predicting precursor molecules for target molecules in organic synthesis.
method Graph-based approach that predicts graph edits and synthons expansion.
result Achieves top-1 accuracy of 53.7%.
A new method embeds tree nodes to vectors for better tree edit distance learning.
problem Learning tree edit distances directly often violates metric axioms and is hard to interpret.
method Adaptive symbol embeddings to learn tree edit distances indirectly.
result Improves tree edit distance learning on multiple datasets.
Many prediction problems can be phrased as inferences over local neighborhoods of graphs. The graph represents the interaction between entities, and the neighborhood of each entity contains information that allows the inferences or predictions. We present an approach for applying machine learning directly to such graph…
ARED introduces a new dataset for Argentina's real estate market.
problem Lack of mixed modality datasets for Argentina's real estate market.
method Developed a comprehensive real estate price prediction dataset series for Argentina.
result Dataset captures time-dependent phenomena on a market level.
Barista simplifies deep neural network design and training.
problem Complexity and error-proneness of editing deep learning frameworks.
method Graphical user interface for Caffe framework.
result Researchers can focus on solving problems without coding.
Graphs of neural networks are represented to preserve symmetry, improving performance across various tasks.
problem Lack of equivariance in neural network representations of other neural networks.
method Represent neural networks as computational graphs and use graph neural networks to preserve permutation symmetry.
result Single model encodes diverse neural architectures, outperforming state-of-the-art methods.
Model removes objects from general scenes using weak supervision.
problem Automatic object removal from general scene images with weak supervision.
method Two-stage editor architecture with mask generator and image in-painter; novel GAN prior for mask generator.
result Effectively removes a wide variety of objects from general scenes using weak supervision.