Search Results for author:
Found 22 papers, 8 papers with code
Improving Prompt Tuning with Learned Prompting Layers
Prompt tuning prepends a soft prompt to the input embeddings or hidden states and only optimizes the prompt to adapt pretrained models (PTMs) to downstream tasks.
Exploring Continual Learning for Code Generation Models
Large-scale code generation models such as Codex and CodeT5 have achieved impressive performance.
Cross-View Hierarchy Network for Stereo Image Super-Resolution
Stereo image super-resolution aims to improve the quality of high-resolution stereo image pairs by exploiting complementary information across views.
DAFD: Domain Adaptation via Feature Disentanglement for Image Classification
This so-called domain shift leads to a significant performance drop in image classification.
Multi-lingual Evaluation of Code Generation Models
Using these benchmarks, we are able to assess the performance of code generation models in a multi-lingual fashion, and discovered generalization ability of language models on out-of-domain languages, advantages of multi-lingual models over mono-lingual, the ability of few-shot prompting to teach the model new languages, and zero-shot translation abilities even on mono-lingual settings.
ContraCLM: Contrastive Learning For Causal Language Model
Specifically, we attain $44\%$ relative improvement on the Semantic Textual Similarity tasks and $34\%$ on Code-to-Code Search tasks.
AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
The homepage of this challenge is at https://github. com/RenYang-home/AIM22_CompressSR.
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and Quantization
Empirical analyses show that, despite the challenging nature of generative tasks, we were able to achieve a 16. 5x model footprint compression ratio with little performance drop relative to the full-precision counterparts on multiple summarization and QA datasets.
New Benchmark for Household Garbage Image Recognition
Household garbage images are usually faced with complex backgrounds, variable illuminations, diverse angles, and changeable shapes, which bring a great difficulty in garbage image classification.
Generating Synthetic Data for Task-Oriented Semantic Parsing with Hierarchical Representations
Finally, we use an auxiliary parser (AP) to filter the generated utterances.
Skeleton Based Action Recognition using a Stacked Denoising Autoencoder with Constraints of Privileged Information
Differing from the previous studies, we propose a new method called Denoising Autoencoder with Temporal and Categorical Constraints (DAE_CTC)} to study the skeletal representation in a view of skeleton reconstruction.
Context-Aware Conversation Thread Detection in Multi-Party Chat
In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs.
Out-of-Domain Detection for Low-Resource Text Classification Tasks
Out-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task.
Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack
We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance.
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
Most approaches to extraction multiple relations from a paragraph require multiple passes over the paragraph.
Ranked #19 on
Relation Extraction
on SemEval-2010 Task 8
FastHybrid: A Hybrid Model for Efficient Answer Selection
In this paper, we propose an extremely efficient hybrid model (FastHybrid) that tackles the problem from both an accuracy and scalability point of view.
Attentive Pooling Networks
In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training.
Ranked #2 on
Question Answering
on SemEvalCQA
LSTM-based Deep Learning Models for Non-factoid Answer Selection
One direction is to define a more composite representation for questions and answers by combining convolutional neural network with the basic framework.
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
We propose a boosting method, DirectBoost, a greedy coordinate descent algorithm that builds an ensemble classifier of weak classifiers through directly minimizing empirical classification error over labeled training examples; once the training classification error is reduced to a local coordinatewise minimum, DirectBoost runs a greedy coordinate ascent algorithm that continuously adds weak classifiers to maximize any targeted arbitrarily defined margins until reaching a local coordinatewise maximum of the margins in a certain sense.
