Search Results for author:
Found 13 papers, 7 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Multi-LoRA Composition for Image Generation
Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models for the accurate rendition of specific elements like distinct characters or unique styles in generated images.
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases
Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs.
Long-tailed Extreme Multi-label Text Classification with Generated Pseudo Label Descriptions
Extreme Multi-label Text Classification (XMTC) has been a tough challenge in machine learning research and applications due to the sheer sizes of the label spaces and the severe data scarce problem associated with the long tail of rare labels in highly skewed distributions.
Multi Label Text Classification
Multi-Label Text Classification
+3
Dict-BERT: Enhancing Language Model Pre-training with Dictionary
In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering
The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.
Improving Hyper-Relational Knowledge Graph Completion
Different from traditional knowledge graphs (KGs) where facts are represented as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets to be associated with additional relation-entity pairs (a. k. a qualifiers) to convey more complex information.
JAKET: Joint Pre-training of Knowledge Graph and Language Understanding
Knowledge graphs (KGs) contain rich information about world knowledge, entities and relations.
Knowledge Embedding Based Graph Convolutional Network
Recently, a considerable literature has grown up around the theme of Graph Convolutional Network (GCN).
Correlation-aware Unsupervised Change-point Detection via Graph Neural Networks
Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
Learning Relation Entailment with Structured and Textual Information
We propose several methods that incorporate both structured and textual information to represent relations for this task.
Graph-Revised Convolutional Network
Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications.
Smartphone App Usage Prediction Using Points of Interest
In this paper we present the first population-level, city-scale analysis of application usage on smartphones.
