Search Results for author:
Found 10 papers, 8 papers with code
Nougat: Neural Optical Understanding for Academic Documents
Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs.
Optical Character Recognition
Optical Character Recognition (OCR)
Llama 2: Open Foundation and Fine-Tuned Chat Models
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters.
Ranked #2 on
Question Answering
on PubChemQA
Galactica: A Large Language Model for Science
We believe these results demonstrate the potential for language models as a new interface for science.
Context-Aware Visual Compatibility Prediction
How do we determine whether two or more clothing items are compatible or visually appealing?
Ranked #1 on
Slot Filling
on Polyvore
Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery
We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition.
Ranked #106 on
Image Classification
on CIFAR-100
(using extra training data)
On the iterative refinement of densely connected representation levels for semantic segmentation
State-of-the-art semantic segmentation approaches increase the receptive field of their models by using either a downsampling path composed of poolings/strided convolutions or successive dilated convolutions.
Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks
Following this trend on visual-based social analysis, we present a novel methodology based on Deep Learning to build a combined image-and-text based personality trait model, trained with images posted together with words found highly correlated to specific personality traits.
A Painless Attention Mechanism for Convolutional Neural Networks
We propose a novel attention mechanism to enhance Convolutional Neural Networks for fine-grained recognition.
Graph Attention Networks
We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Ranked #1 on
Node Classification
on Pubmed
(Validation metric)
Regularizing CNNs with Locally Constrained Decorrelations
In this paper, we show that regularizing negatively correlated features is an obstacle for effective decorrelation and present OrthoReg, a novel regularization technique that locally enforces feature orthogonality.
