Search Results for author:
Found 19 papers, 10 papers with code
MixSumm: Topic-based Data Augmentation using LLMs for Low-resource Extractive Text Summarization
Low-resource extractive text summarization is a vital but heavily underexplored area of research.
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks
Our method carefully selects and updates a very small subset of the trainable parameters without altering any part of its architecture and training procedure.
WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?
We study the use of large language model-based agents for interacting with software via web browsers.
LitLLM: A Toolkit for Scientific Literature Review
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work.
StarVector: Generating Scalable Vector Graphics Code from Images
These visual tokens are pre-pended to the SVG token embeddings, and the sequence is modeled by the StarCoder model using next-token prediction, effectively learning to align the visual and code tokens.
LLM aided semi-supervision for Extractive Dialog Summarization
Generating high-quality summaries for chat dialogs often requires large labeled datasets.
Prompt-based Pseudo-labeling Strategy for Sample-Efficient Semi-Supervised Extractive Summarization
While SSL is popular for image and text classification, it is relatively underexplored for the task of extractive text summarization.
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation
Our specific PromptMix method consists of two steps: 1) generate challenging text augmentations near class boundaries; however, generating borderline examples increases the risk of false positives in the dataset, so we 2) relabel the text augmentations using a prompting-based LLM classifier to enhance the correctness of labels in the generated data.
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity.
A Deep Learning Localization Method for Measuring Abdominal Muscle Dimensions in Ultrasound Images
US images can be used to measure abdominal muscles dimensions for the diagnosis and creation of customized treatment plans for patients with Low Back Pain (LBP), however, they are difficult to interpret.
Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations
In computer vision applications, most methods explain models by displaying the regions in the input image that they focus on for their prediction, but it is difficult to improve models based on these explanations since they do not indicate why the model fail.
Adaptive Gradient Methods Converge Faster with Over-Parameterization (and you can do a line-search)
Under an interpolation assumption, we prove that AMSGrad with a constant step-size and momentum can converge to the minimizer at the faster $O(1/T)$ rate for smooth, convex functions.
A Realistic Fish-Habitat Dataset to Evaluate Algorithms for Underwater Visual Analysis
The dataset consists of approximately 40 thousand images collected underwater from 20 \green{habitats in the} marine-environments of tropical Australia.
LOOC: Localize Overlapping Objects with Count Supervision
For localization, LOOC achieves a strong new baseline in the novel problem setup where only count supervision is available.
Where are the Masks: Instance Segmentation with Image-level Supervision
A major obstacle in instance segmentation is that existing methods often need many per-pixel labels in order to be effective.
Ranked #9 on
Image-level Supervised Instance Segmentation
on PASCAL VOC 2012 val
(using extra training data)
Image-level Supervised Instance Segmentation
Semantic Segmentation
Instance Segmentation with Point Supervision
Instance segmentation methods often require costly per-pixel labels.
Efficient Deep Gaussian Process Models for Variable-Sized Input
The key advantage is that the combination of GP and DRF leads to a tractable model that can both handle a variable-sized input as well as learn deep long-range dependency structures of the data.
Where are the Blobs: Counting by Localization with Point Supervision
However, we propose a detection-based method that does not need to estimate the size and shape of the objects and that outperforms regression-based methods.
Ranked #1 on
Object Counting
on Pascal VOC 2007 count-test
Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection
There has been significant recent work on the theory and application of randomized coordinate descent algorithms, beginning with the work of Nesterov [SIAM J.
