Search Results for author:
Found 33 papers, 13 papers with code
CodecLM: Aligning Language Models with Tailored Synthetic Data
To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.
Unsupervised LLM Adaptation for Question Answering
In this task, we leverage a pre-trained LLM, a publicly available QA dataset (source data), and unlabeled documents from the target domain.
Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding
We propose the Chain-of-Table framework, where tabular data is explicitly used in the reasoning chain as a proxy for intermediate thoughts.
Ranked #3 on
Table-based Fact Verification
on TabFact
Tool Documentation Enables Zero-Shot Tool-Usage with Large Language Models
Today, large language models (LLMs) are taught to use new tools by providing a few demonstrations of the tool's usage.
FormNetV2: Multimodal Graph Contrastive Learning for Form Document Information Extraction
In FormNetV2, we introduce a centralized multimodal graph contrastive learning strategy to unify self-supervised pre-training for all modalities in one loss.
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset.
Multimodal Prompting with Missing Modalities for Visual Recognition
In this paper, we tackle two challenges in multimodal learning for visual recognition: 1) when missing-modality occurs either during training or testing in real-world situations; and 2) when the computation resources are not available to finetune on heavy transformer models.
Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrieval
Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image.
Ranked #1 on
Zero-shot Image Retrieval
on ImageNet-R
Neural Spline Search for Quantile Probabilistic Modeling
Accurate estimation of output quantiles is crucial in many use cases, where it is desired to model the range of possibility.
VRDU: A Benchmark for Visually-rich Document Understanding
Understanding visually-rich business documents to extract structured data and automate business workflows has been receiving attention both in academia and industry.
QueryForm: A Simple Zero-shot Form Entity Query Framework
Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.
Prefix Conditioning Unifies Language and Label Supervision
In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
FormNet: Structural Encoding beyond Sequential Modeling in Form Document Information Extraction
Sequence modeling has demonstrated state-of-the-art performance on natural language and document understanding tasks.
Towards Group Robustness in the presence of Partial Group Labels
Such a requirement is impractical in situations where the data labeling efforts for minority or rare groups are significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
Anomaly Clustering: Grouping Images into Coherent Clusters of Anomaly Types
We define a distance function between images, each of which is represented as a bag of embeddings, by the Euclidean distance between weighted averaged embeddings.
Learning to Prompt for Continual Learning
The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning
While remarkable progress in imbalanced supervised learning has been made recently, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only is a few labeled data provided, but the underlying data distribution can be severely imbalanced.
Invariant Learning with Partial Group Labels
Such a requirement is impractical in situations where the data labelling efforts for minority or rare groups is significantly laborious or where the individuals comprising the dataset choose to conceal sensitive information.
ROPE: Reading Order Equivariant Positional Encoding for Graph-based Document Information Extraction
Natural reading orders of words are crucial for information extraction from form-like documents.
Self-supervise, Refine, Repeat: Improving Unsupervised Anomaly Detection
We demonstrate our method on various unsupervised AD tasks with image and tabular data.
Learning from Weakly-labeled Web Videos via Exploring Sub-Concepts
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
Exploring Sub-Pseudo Labels for Learning from Weakly-Labeled Web Videos
To address this issue, we introduce a new method for pre-training video action recognition models using queried web videos.
Learning to Branch for Multi-Task Learning
Training multiple tasks jointly in one deep network yields reduced latency during inference and better performance over the single-task counterpart by sharing certain layers of a network.
A Simple Semi-Supervised Learning Framework for Object Detection
Semi-supervised learning (SSL) has a potential to improve the predictive performance of machine learning models using unlabeled data.
Ranked #13 on
Semi-Supervised Object Detection
on COCO 100% labeled data
(using extra training data)
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
In this work, we connect two distinct concepts for unsupervised domain adaptation: feature distribution alignment between domains by utilizing the task-specific decision boundary and the Wasserstein metric.
Ranked #19 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challenging to train properly.
RoomNet: End-to-End Room Layout Estimation
This paper focuses on the task of room layout estimation from a monocular RGB image.
Recursive Recurrent Nets with Attention Modeling for OCR in the Wild
We present recursive recurrent neural networks with attention modeling (R$^2$AM) for lexicon-free optical character recognition in natural scene images.
Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree
We seek to improve deep neural networks by generalizing the pooling operations that play a central role in current architectures.
Ranked #17 on
Image Classification
on MNIST
Training Deeper Convolutional Networks with Deep Supervision
One of the most promising ways of improving the performance of deep convolutional neural networks is by increasing the number of convolutional layers.
Deeply-Supervised Nets
Our proposed deeply-supervised nets (DSN) method simultaneously minimizes classification error while making the learning process of hidden layers direct and transparent.
Ranked #23 on
Image Classification
on MNIST
Region-based Discriminative Feature Pooling for Scene Text Recognition
We present a new feature representation method for scene text recognition problem, particularly focusing on improving scene character recognition.
