Search Results for author:
Found 16 papers, 9 papers with code
AltDiffusion: A Multilingual Text-to-Image Diffusion Model
Specifically, we first train a multilingual text encoder based on the knowledge distillation.
UniTabE: A Universal Pretraining Protocol for Tabular Foundation Model in Data Science
This research primarily centers on classification and regression tasks involving tabular data, and conducts rigorous experimental testing and analyses to validate the effectiveness of our methodology.
EVA-CLIP: Improved Training Techniques for CLIP at Scale
Our approach incorporates new techniques for representation learning, optimization, and augmentation, enabling EVA-CLIP to achieve superior performance compared to previous CLIP models with the same number of parameters but significantly smaller training costs.
Ranked #4 on
Zero-Shot Transfer Image Classification
on Food-101
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data.
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
In this work, we present a conceptually simple and effective method to train a strong bilingual/multilingual multimodal representation model.
PTab: Using the Pre-trained Language Model for Modeling Tabular Data
The learning of an effective contextual representation requires meaningful features and a large amount of data.
Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer
In order to unify these two stages, we explore a model-based indexer for document retrieval.
DynamicRetriever: A Pre-training Model-based IR System with Neither Sparse nor Dense Index
Web search provides a promising way for people to obtain information and has been extensively studied.
Calculating Question Similarity is Enough: A New Method for KBQA Tasks
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with the help of an external knowledge base.
Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking
We introduce Dynaboard, an evaluation-as-a-service framework for hosting benchmarks and conducting holistic model comparison, integrated with the Dynabench platform.
Multilingual Autoregressive Entity Linking
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Ranked #2 on
Entity Disambiguation
on Mewsli-9
(using extra training data)
Multi-Dimensional Gender Bias Classification
We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.
Dense Passage Retrieval for Open-Domain Question Answering
Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method.
Ranked #1 on
Question Answering
on NaturalQA
Scalable Zero-shot Entity Linking with Dense Entity Retrieval
This paper introduces a conceptually simple, scalable, and highly effective BERT-based entity linking model, along with an extensive evaluation of its accuracy-speed trade-off.
PyTorch-BigGraph: A Large-scale Graph Embedding System
Graph embedding methods produce unsupervised node features from graphs that can then be used for a variety of machine learning tasks.
Ranked #1 on
Link Prediction
on YouTube
(Macro F1 metric)
StarSpace: Embed All The Things!
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
