A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
ED-NeRF efficiently edits 3D scenes using latent space NeRF and improved loss functions.
NeRF-VAE generates 3D scenes with geometric structure from few images.
StyleNeRF generates high-resolution images with 3D consistency and style control.
Proposes a model to generate 3D-aware images from 2D images.
ObSuRF converts a single image into a 3D model with NeRFs.
DreamFusion uses text-to-image diffusion models to create 3D images efficiently.
BPI models 2D patterns on multiple planes and 3D scene from a single image.
Paper develops a new method to analyze 3D tree-like objects.
Overview of 3D TQFTs and 3-manifold invariants.
Recent progress in deep generative models has led to tremendous breakthroughs in image generation. However, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3D world. We present a new generative model, Visual Object Networks (VON), synthesizing natural images of o…
We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yie…
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…
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project. Existing solutions either rely on a user reputation system or consist of a highly specialized predictor that is tailored to …
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architecture…
Edit distance, also known as Levenshtein distance, is an essential way to compare two strings that proved to be particularly useful in the analysis of genetic sequences and natural language processing. However, edit distance is a discrete function that is known to be hard to optimize. This fact hampers the use of this …
Ground-A-Video edits videos without training, preserving intended changes.
Paper fine-tunes LLMs using user edits, unifying preference, supervision, and reward feedback.
Programming languages are emerging as a challenging and interesting domain for machine learning. A core task, which has received significant attention in recent years, is building generative models of source code. However, to our knowledge, previous generative models have always been framed in terms of generating stati…
We propose a new generative model of sentences that first samples a prototype sentence from the training corpus and then edits it into a new sentence. Compared to traditional models that generate from scratch either left-to-right or by first sampling a latent sentence vector, our prototype-then-edit model improves perp…
MEGAN models chemical reactions as graph edits, improving synthesis planning.
New method uses coupled SDEs to edit images with high fidelity and consistency.
ViewFool identifies adversarial viewpoints to test image recognition robustness.
RS-Del provides robustness for sequence classifiers against edit distance attacks.
Paper proposes efficient image inversion and editing using rectified stochastic differential equations.
New method detects watermarks in LLM-generated text with human edits.
A technique to quickly fix mistakes in neural networks.
For the task of generating complex outputs such as source code, editing existing outputs can be easier than generating complex outputs from scratch. With this motivation, we propose an approach that first retrieves a training example based on the input (e.g., natural language description) and then edits it to the desir…
Machine learning experiments show IID assumption is flawed for bathymetry editing.
GMED edits stored examples to improve continual learning.
Facial attribute editing aims to manipulate single or multiple attributes of a face image, i.e., to generate a new face with desired attributes while preserving other details. Recently, generative adversarial net (GAN) and encoder-decoder architecture are usually incorporated to handle this task with promising results.…
New seq2seq model can copy entire spans, outperforming simpler models in editing tasks.
Professional-grade software applications are powerful but complicatedexpert 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.
PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.
ARED introduces a new dataset for Argentina's real estate market.
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as m…
The Wikimedia Foundation has recently observed that newly joining editors on Wikipedia are increasingly failing to integrate into the Wikipedia editors' community, i.e. the community is becoming increasingly harder to penetrate. To sustain healthy growth of the community, the Wikimedia Foundation aims to quantitatively…
Many machine learning models can be attacked with adversarial examples, i.e. inputs close to correctly classified examples that are classified incorrectly. However, most research on adversarial attacks to date is limited to vectorial data, in particular image data. In this contribution, we extend the field by introduci…
The paper introduces COAR to estimate component attributions and enable model editing.
The study examines Euclid's Book I, focusing on area applications and construction methods.
Localized Multidirectional Correction improves non-refusal target-response behavior in foundation models.
Causal KL improves on existing metrics for evaluating causal models.
While neural sequence generation models achieve initial success for many NLP applications, the canonical decoding procedure with left-to-right generation order (i.e., autoregressive) in one-pass can not reflect the true nature of human revising a sentence to obtain a refined result. In this work, we propose XL-Editor, …
Metric learning has the aim to improve classification accuracy by learning a distance measure which brings data points from the same class closer together and pushes data points from different classes further apart. Recent research has demonstrated that metric learning approaches can also be applied to trees, such as m…
This paper tackles disentanglement in image editing and reconstruction.
This is a survey written for a special edition of the journal of differential geometry.
We consider the setting of Reeb graphs of piecewise linear functions and study distances between them that are stable, meaning that functions which are similar in the supremum norm ought to have similar Reeb graphs. We define an edit distance for Reeb graphs and prove that it is stable and universal, meaning that it pr…
Proposes CLSM for better subsequence generation in music sequences.