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Found 32 papers, 4 papers with code
Making Harmful Behaviors Unlearnable for Large Language Models
Specifically, we introduce ``security vectors'', a few new parameters that can be separated from the LLM, to ensure LLM's responses are consistent with the harmful behavior.
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model.
COGEDAP: A COmprehensive GEnomic Data Analysis Platform
The analyses can be done by running these auto-generated scripts with the software containers.
Joint RIS Calibration and Multi-User Positioning
Reconfigurable intelligent surfaces (RISs) are expected to be a key component enabling the mobile network evolution towards a flexible and intelligent 6G wireless platform.
Nonparametric Decoding for Generative Retrieval
The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed.
The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago
To this end, this study applies difference-in-differences (i. e., a regression-based causal inference approach) to empirically evaluate the effects of the congestion tax policy on ridesourcing demand and traffic congestion in Chicago.
DePS: An improved deep learning model for de novo peptide sequencing
De novo peptide sequencing from mass spectrometry data is an important method for protein identification.
Active Learning Over Multiple Domains in Natural Language Tasks
Studies of active learning traditionally assume the target and source data stem from a single domain.
C+1 Loss: Learn to Classify C Classes of Interest and the Background Class Differentially
We figure out that the background class should be treated differently from the classes of interest during training.
Joint Positioning and Tracking via NR Sidelink in 5G-Empowered Industrial IoT: Releasing the Potential of V2X Technology
The fifth generation (5G) mobile networks with enhanced connectivity and positioning capabilities play an increasingly important role in the development of automated vehicle-to-everything (V2X) and other advanced industrial Internet of Things (IoT) systems.
The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab.
On the Transferability of Minimal Prediction Preserving Inputs in Question Answering
In the context of question answering, we investigate competing hypotheses for the existence of MPPIs, including poor posterior calibration of neural models, lack of pretraining, and "dataset bias" (where a model learns to attend to spurious, non-generalizable cues in the training data).
MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering
Progress in cross-lingual modeling depends on challenging, realistic, and diverse evaluation sets.
ContourRend: A Segmentation Method for Improving Contours by Rendering
A good object segmentation should contain clear contours and complete regions.
Prob2Vec: Mathematical Semantic Embedding for Problem Retrieval in Adaptive Tutoring
We propose a hierarchical problem embedding algorithm, called Prob2Vec, that consists of abstraction and embedding steps.
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
Graph-FCN for image semantic segmentation
Then we apply graph convolutional network to solve this graph node classification problem.
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering
To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.
NCUEE at MEDIQA 2019: Medical Text Inference Using Ensemble BERT-BiLSTM-Attention Model
This study describes the model design of the NCUEE system for the MEDIQA challenge at the ACL-BioNLP 2019 workshop.
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
UM-Corpus: A Large English-Chinese Parallel Corpus for Statistical Machine Translation
This paper describes the acquisition of a large scale and high quality parallel corpora for English and Chinese.
