Search Results for author:
Found 18 papers, 11 papers with code
The Llama 3 Herd of Models
This paper presents a new set of foundation models, called Llama 3.
Ranked #3 on
Multi-task Language Understanding
on MMLU
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
Multilingual Speech-to-Speech Translation into Multiple Target Languages
Despite a few studies on multilingual S2ST, their focus is the multilinguality on the source side, i. e., the translation from multiple source languages to one target language.
Revisiting Machine Translation for Cross-lingual Classification
Machine Translation (MT) has been widely used for cross-lingual classification, either by translating the test set into English and running inference with a monolingual model (translate-test), or translating the training set into the target languages and finetuning a multilingual model (translate-train).
Towards Being Parameter-Efficient: A Stratified Sparsely Activated Transformer with Dynamic Capacity
Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token.
MuAViC: A Multilingual Audio-Visual Corpus for Robust Speech Recognition and Robust Speech-to-Text Translation
We introduce MuAViC, a multilingual audio-visual corpus for robust speech recognition and robust speech-to-text translation providing 1200 hours of audio-visual speech in 9 languages.
Audio-Visual Speech Recognition
Robust Speech Recognition
+4
Language-Aware Multilingual Machine Translation with Self-Supervised Learning
In this work, we first investigate how to utilize intra-distillation to learn more *language-specific* parameters and then show the importance of these language-specific parameters.
Causes and Cures for Interference in Multilingual Translation
Multilingual machine translation models can benefit from synergy between different language pairs, but also suffer from interference.
No Language Left Behind: Scaling Human-Centered Machine Translation
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today.
Ranked #1 on
Machine Translation
on IWSLT2017 French-English
(SacreBLEU metric)
FLAVA: A Foundational Language And Vision Alignment Model
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks.
Ranked #4 on
Image Retrieval
on MS COCO
Tricks for Training Sparse Translation Models
Multi-task learning with an unbalanced data distribution skews model learning towards high resource tasks, especially when model capacity is fixed and fully shared across all tasks.
Human-Adversarial Visual Question Answering
Human subjects interact with a state-of-the-art VQA model, and for each image in the dataset, attempt to find a question where the model's predicted answer is incorrect.
Creative Sketch Generation
Sketching or doodling is a popular creative activity that people engage in.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes
This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes.
Ranked #10 on
Meme Classification
on Hateful Memes
MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond
This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e. g. a question or a category).
Ranked #1 on
Object Counting
on HowMany-QA
Are we pretraining it right? Digging deeper into visio-linguistic pretraining
Surprisingly, we show that automatically generated data in a domain closer to the downstream task (e. g., VQA v2) is a better choice for pretraining than "natural" data but of a slightly different domain (e. g., Conceptual Captions).
12-in-1: Multi-Task Vision and Language Representation Learning
Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded language understanding skills required for success at these tasks overlap significantly.
Ranked #1 on
10-shot image generation
on MEAD
Only Time Can Tell: Discovering Temporal Data for Temporal Modeling
However, in current video datasets it has been observed that action classes can often be recognized without any temporal information from a single frame of video.
