Search Results for author:
Found 35 papers, 26 papers with code
BigDocs: An Open and Permissively-Licensed Dataset for Training Multimodal Models on Document and Code Tasks
We use an efficient data curation process to ensure our data is high-quality and license-permissive.
Fast Convergence of Softmax Policy Mirror Ascent
Natural policy gradient (NPG) is a common policy optimization algorithm and can be viewed as mirror ascent in the space of probabilities.
IntentGPT: Few-shot Intent Discovery with Large Language Models
IntentGPT comprises an \textit{In-Context Prompt Generator}, which generates informative prompts for In-Context Learning, an \textit{Intent Predictor} for classifying and discovering user intents from utterances, and a \textit{Semantic Few-Shot Sampler} that selects relevant few-shot examples and a set of known intents to be injected into the prompt.
Capture the Flag: Uncovering Data Insights with Large Language Models
The extraction of a small number of relevant insights from vast amounts of data is a crucial component of data-driven decision-making.
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
In the present work, we describe Top-K K-Nearest Neighbor (TK-KNN), which uses a more robust pseudo-labeling approach based on distance in the embedding space while maintaining a balanced set of pseudo-labeled examples across classes through a ranking-based approach.
Automatic Data Augmentation Learning using Bilevel Optimization for Histopathological Images
Experimental results show that our model can learn color and affine transformations that are more helpful to train an image classifier than predefined DA transformations, which are also more expensive as they need to be selected before the training by grid search on a validation set.
FigGen: Text to Scientific Figure Generation
The generative modeling landscape has experienced tremendous growth in recent years, particularly in generating natural images and art.
Language Decision Transformers with Exponential Tilt for Interactive Text Environments
Text-based game environments are challenging because agents must deal with long sequences of text, execute compositional actions using text and learn from sparse rewards.
OCR-VQGAN: Taming Text-within-Image Generation
To alleviate this problem, we present OCR-VQGAN, an image encoder, and decoder that leverages OCR pre-trained features to optimize a text perceptual loss, encouraging the architecture to preserve high-fidelity text and diagram structure.
Constraining Representations Yields Models That Know What They Don't Know
In the add-on case, the original neural network's inference head is completely unaffected (so its accuracy remains the same) but we now have the option to use TAC's own confidence and prediction when determining which course of action to take in an hypothetical production workflow.
Workflow Discovery from Dialogues in the Low Data Regime
We propose and evaluate an approach that conditions models on the set of possible actions, and we show that using this strategy, we can improve WD performance.
Ranked #1 on
Workflow Discovery
on ABCD
Overcoming challenges in leveraging GANs for few-shot data augmentation
In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance.
Kubric: A scalable dataset generator
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
Neural Point Light Fields
We introduce Neural Point Light Fields that represent scenes implicitly with a light field living on a sparse point cloud.
Stochastic Mirror Descent: Convergence Analysis and Adaptive Variants via the Mirror Stochastic Polyak Stepsize
We investigate the convergence of stochastic mirror descent (SMD) under interpolation in relatively smooth and smooth convex optimization.
A Survey of Self-Supervised and Few-Shot Object Detection
Labeling data is often expensive and time-consuming, especially for tasks such as object detection and instance segmentation, which require dense labeling of the image.
SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction
In order to obtain the viewpoints for these unlabeled images, we propose to use a Siamese network that takes two images as input and outputs whether they correspond to the same viewpoint.
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations
Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems.
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8. 8\% of greenhouse gas emissions worldwide.
Affinity LCFCN: Learning to Segment Fish with Weak Supervision
Leading automatic approaches rely on fully-supervised segmentation models to acquire these measurements but these require collecting per-pixel labels -- also time consuming and laborious: i. e., it can take up to two minutes per fish to generate accurate segmentation labels, almost always requiring at least some manual intervention.
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images.
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection.
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.
Learning Data Augmentation with Online Bilevel Optimization for Image Classification
Data augmentation is a key practice in machine learning for improving generalization performance.
Adaptive Gradient Methods Converge Faster with Over-Parameterization (but you should do a line-search)
In this setting, we prove that AMSGrad with constant step-size and momentum converges to the minimizer at a faster $O(1/T)$ rate.
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
We propose Continual-MAML, an online extension of the popular MAML algorithm as a strong baseline for this scenario.
Embedding Propagation: Smoother Manifold for Few-Shot Classification
Furthermore, we show that embedding propagation consistently improves the accuracy of the models in multiple semi-supervised learning scenarios by up to 16\% points.
Stochastic Polyak Step-size for SGD: An Adaptive Learning Rate for Fast Convergence
Consequently, the proposed stochastic Polyak step-size (SPS) is an attractive choice for setting the learning rate for stochastic gradient descent (SGD).
Fast and Furious Convergence: Stochastic Second Order Methods under Interpolation
Under this condition, we show that the regularized subsampled Newton method (R-SSN) achieves global linear convergence with an adaptive step-size and a constant batch-size.
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time.
Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates
To improve the proposed methods' practical performance, we give heuristics to use larger step-sizes and acceleration.
M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
Unsupervised domain adaptation techniques have been successful for a wide range of problems where supervised labels are limited.
Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence
Block coordinate descent (BCD) methods are widely used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure.
Optimization and Control 90C06
