Search Results for author:
Found 19 papers, 9 papers with code
Multi-Patch Prediction: Adapting LLMs for Time Series Representation Learning
In this study, we present aLLM4TS, an innovative framework that adapts Large Language Models (LLMs) for time-series representation learning.
CFBenchmark: Chinese Financial Assistant Benchmark for Large Language Model
Large language models (LLMs) have demonstrated great potential in the financial domain.
DreamCom: Finetuning Text-guided Inpainting Model for Image Composition
The goal of image composition is merging a foreground object into a background image to obtain a realistic composite image.
CFGPT: Chinese Financial Assistant with Large Language Model
The CFAPP is centered on large language models (LLMs) and augmented with additional modules to ensure multifaceted functionality in real-world application.
Deep Image Harmonization in Dual Color Spaces
The network comprises a $RGB$ harmonization backbone, an $Lab$ encoding module, and an $Lab$ control module.
Ranked #1 on
Image Harmonization
on HAdobe5k(1024$\times$1024)
Painterly Image Harmonization using Diffusion Model
Painterly image harmonization aims to insert photographic objects into paintings and obtain artistically coherent composite images.
Knowledge Proxy Intervention for Deconfounded Video Question Answering
To tackle the challenge that the confounder in VideoQA is unobserved and non-enumerable in general, we propose a model-agnostic framework called Knowledge Proxy Intervention (KPI), which introduces an extra knowledge proxy variable in the causal graph to cut the backdoor path and remove the confounder.
From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering
We hope that Causal-VidQA can guide the research of video understanding from representation learning to deeper reasoning.
Fast Object Placement Assessment
However, given a pair of scaled foreground and background, to enumerate all the reasonable locations, existing OPA model needs to place the foreground at each location on the background and pass the obtained composite image through the model one at a time, which is very time-consuming.
OPA: Object Placement Assessment Dataset
Image composition aims to generate realistic composite image by inserting an object from one image into another background image, where the placement (e. g., location, size, occlusion) of inserted object may be unreasonable, which would significantly degrade the quality of the composite image.
Zero-Shot Sketch-Based Image Retrieval with Structure-aware Asymmetric Disentanglement
The goal of Sketch-Based Image Retrieval (SBIR) is using free-hand sketches to retrieve images of the same category from a natural image gallery.
Subword ELMo
Embedding from Language Models (ELMo) has shown to be effective for improving many natural language processing (NLP) tasks, and ELMo takes character information to compose word representation to train language models. However, the character is an insufficient and unnatural linguistic unit for word representation. Thus we introduce Embedding from Subword-aware Language Models (ESuLMo) which learns word representation from subwords using unsupervised segmentation over words. We show that ESuLMo can enhance four benchmark NLP tasks more effectively than ELMo, including syntactic dependency parsing, semantic role labeling, implicit discourse relation recognition and textual entailment, which brings a meaningful improvement over ELMo.
Lattice-Based Transformer Encoder for Neural Machine Translation
To integrate different segmentations with the state-of-the-art NMT model, Transformer, we propose lattice-based encoders to explore effective word or subword representation in an automatic way during training.
Judging Chemical Reaction Practicality From Positive Sample only Learning
Chemical reaction practicality is the core task among all symbol intelligence based chemical information processing, for example, it provides indispensable clue for further automatic synthesis route inference.
Effective Subword Segmentation for Text Comprehension
Representation learning is the foundation of machine reading comprehension and inference.
Fast Neural Chinese Word Segmentation for Long Sentences
Rapidly developed neural models have achieved competitive performance in Chinese word segmentation (CWS) as their traditional counterparts.
Lingke: A Fine-grained Multi-turn Chatbot for Customer Service
Traditional chatbots usually need a mass of human dialogue data, especially when using supervised machine learning method.
Modeling Multi-turn Conversation with Deep Utterance Aggregation
In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation.
Ranked #13 on
Conversational Response Selection
on E-commerce
SJTU-NLP at SemEval-2018 Task 9: Neural Hypernym Discovery with Term Embeddings
This paper describes a hypernym discovery system for our participation in the SemEval-2018 Task 9, which aims to discover the best (set of) candidate hypernyms for input concepts or entities, given the search space of a pre-defined vocabulary.
Ranked #5 on
Hypernym Discovery
on Music domain
