Search Results for author:
Found 19 papers, 10 papers with code
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoder
Distillation aims to distill knowledge from a given model (a. k. a the teacher net) and transfer it to another (a. k. a the student net).
A Prompt Learning Framework for Source Code Summarization
PromptCS trains a prompt agent that can generate continuous prompts to unleash the potential for LLMs in code summarization.
Towards Automatic Power Battery Detection: New Challenge, Benchmark Dataset and Baseline
We conduct a comprehensive study on a new task named power battery detection (PBD), which aims to localize the dense cathode and anode plates endpoints from X-ray images to evaluate the quality of power batteries.
Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?
To do so, we compare the performance of models trained with code token sequence (Token for short) based code representation and AST-based code representation on three popular types of code-related tasks.
TransformCode: A Contrastive Learning Framework for Code Embedding via Subtree Transformation
Our framework has several advantages over existing methods: (1) It is flexible and adaptable, because it can easily be extended to other downstream tasks that require code representation (such as code-clone detection and classification); (2) it is efficient and scalable, because it does not require a large model or a large amount of training data, and it can support any programming language; (3) it is not limited to unsupervised learning, but can also be applied to some supervised learning tasks by incorporating task-specific labels or objectives; and (4) it can also adjust the number of encoder parameters based on computing resources.
Contrastive Multi-Level Graph Neural Networks for Session-based Recommendation
CM-GNN further introduces an attention-based fusion module to learn pairwise relation-based session representation by fusing the item representations generated by L-GCN and G-GCN.
Tracking Anything in High Quality
To further improve the quality of tracking masks, a pretrained MR model is employed to refine the tracking results.
Ranked #5 on
Semi-Supervised Video Object Segmentation
on YouTube-VOS 2019
(using extra training data)
Benchmarking Robustness of AI-Enabled Multi-sensor Fusion Systems: Challenges and Opportunities
Multi-Sensor Fusion (MSF) based perception systems have been the foundation in supporting many industrial applications and domains, such as self-driving cars, robotic arms, and unmanned aerial vehicles.
3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW
In this paper, we introduce 3rd place solution for PVUW2023 VSS track.
Automatic Code Summarization via ChatGPT: How Far Are We?
To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet.
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama.
Certifying Robustness of Convolutional Neural Networks with Tight Linear Approximation
The robustness of neural network classifiers is becoming important in the safety-critical domain and can be quantified by robustness verification.
PSP: Million-level Protein Sequence Dataset for Protein Structure Prediction
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
Quantifying Unknown Quantum Entanglement via a Hybrid Quantum-Classical Machine Learning Framework
Quantifying unknown quantum entanglement experimentally is a difficult task, but also becomes more and more necessary because of the fast development of quantum engineering.
Deep Graph Learning for Spatially-Varying Indoor Lighting Prediction
At its core is a new lighting model (dubbed DSGLight) based on depth-augmented Spherical Gaussians (SG) and a Graph Convolutional Network (GCN) that infers the new lighting representation from a single LDR image of limited field-of-view.
Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
Recent attempts solve this problem by learning static representations of entities and references, ignoring their dynamic properties, i. e., entities may exhibit diverse roles within task relations, and references may make different contributions to queries.
Software Engineering Practice in the Development of Deep Learning Applications
Specifically, we asked the respondents to identify lacks and challenges in the practice of the development life cycle of DL applications.
Software Engineering
From Data Quality to Model Quality: an Exploratory Study on Deep Learning
Nowadays, people strive to improve the accuracy of deep learning models.
A Preliminary Study on Data Augmentation of Deep Learning for Image Classification
Deep learning models have a large number of freeparameters that need to be calculated by effective trainingof the models on a great deal of training data to improvetheir generalization performance.
