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 #107 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
Heterogeneous Node Classification
on DBLP (Heterogeneous Node Classification)
(Macro-F1 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.
