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Found 35 papers, 12 papers with code
HUB@DravidianLangTech-EACL2021: Meme Classification for Tamil Text-Image Fusion
The data of the meme classification task is the fusion data of text and image information.
Automatic Detecting for Health-related Twitter Data with BioBERT
Social media used for health applications usually contains a large amount of data posted by users, which brings various challenges to NLP, such as spoken language, spelling errors, novel/creative phrases, etc.
TEAM HUB@LT-EDI-EACL2021: Hope Speech Detection Based On Pre-trained Language Model
The data set in the shared task consists of three different languages (English, Tamil, and Malayalam).
HUB@DravidianLangTech-EACL2021: Identify and Classify Offensive Text in Multilingual Code Mixing in Social Media
Among the known tasks related to offensive speech detection, this is the first task to detect offensive comments posted in social media comments in the Dravidian language.
Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection
In this paper, we revisit dataset ownership verification.
150 Years of Return Predictability Around the World: A Holistic View
The predictive regressions based on the VAR analysis by Cochrane (2008, 2011) suggest that 14 (5) countries have predictable payout growth in the equity (housing) markets, ex., the dividend price predicts the dividend growth in the US.
MOVE: Effective and Harmless Ownership Verification via Embedded External Features
In general, we conduct the ownership verification by verifying whether a suspicious model contains the knowledge of defender-specified external features.
Action Quality Assessment with Temporal Parsing Transformer
Action Quality Assessment(AQA) is important for action understanding and resolving the task poses unique challenges due to subtle visual differences.
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
Inspired by the vulnerability of DNNs on adversarial perturbations, we propose a novel defence mechanism by adversarial machine learning for good.
Residual Local Feature Network for Efficient Super-Resolution
Moreover, we revisit the popular contrastive loss and observe that the selection of intermediate features of its feature extractor has great influence on the performance.
Adaptive Frequency Learning in Two-branch Face Forgery Detection
Face forgery has attracted increasing attention in recent applications of computer vision.
Moses: Efficient Exploitation of Cross-device Transferable Features for Tensor Program Optimization
Achieving efficient execution of machine learning models has attracted significant attention recently.
PCL: Proxy-Based Contrastive Learning for Domain Generalization
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #9 on
Domain Generalization
on PACS
Automated Customization of On-Thing Inference for Quality-of-Experience Enhancement
The core of our method is a DNN selection module that learns user QoE patterns on-the-fly and identifies the best-fit DNN for on-thing inference with the learned knowledge.
Clustering Effect of Adversarial Robust Models
Adversarial robustness has received increasing attention along with the study of adversarial examples.
Clustering Effect of (Linearized) Adversarial Robust Models
Adversarial robustness has received increasing attention along with the study of adversarial examples.
More Than Reading Comprehension: A Survey on Datasets and Metrics of Textual Question Answering
Textual Question Answering (QA) aims to provide precise answers to user's questions in natural language using unstructured data.
Discriminative Latent Semantic Graph for Video Captioning
Video captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video.
Machine Learning Classification Methods and Portfolio Allocation: An Examination of Market Efficiency
We provide unique economic insights about OOS predictability and machine learning models.
hub at SemEval-2021 Task 7: Fusion of ALBERT and Word Frequency Information Detecting and Rating Humor and Offense
The final scores of the prediction results of the two subtask test sets submitted by our team are task1a 0. 921 (F1), task1a 0. 9364 (Accuracy), task1b 0. 6288 (RMSE), task1c 0. 5333 (F1), task1c 0. 0. 5591 (Accuracy), and task2 0. 5027 (RMSE) respectively.
hub at SemEval-2021 Task 1: Fusion of Sentence and Word Frequency to Predict Lexical Complexity
Use Inception block as a shared layer to learn sentence and word frequency information We described the implementation of our best system and discussed our methods and experiments in the task.
hub at SemEval-2021 Task 2: Word Meaning Similarity Prediction Model Based on RoBERTa and Word Frequency
What we need to do is to use our method to determine as accurately as possible the meaning of the words in a sentence pair are the same or different.
hub at SemEval-2021 Task 5: Toxic Span Detection Based on Word-Level Classification
This article introduces the system description of the hub team, which explains the related work and experimental results of our team{'}s participation in SemEval 2021 Task 5: Toxic Spans Detection.
Improving Adversarial Robustness via Channel-wise Activation Suppressing
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).
Fast and Efficient DNN Deployment via Deep Gaussian Transfer Learning
Firstly, a deep Gaussian process (DGP) model is built on the historical data to learn empirical knowledge.
DEEPYANG at SemEval-2020 Task 4: Using the Hidden Layer State of BERT Model for Differentiating Common Sense
Introducing common sense to natural language understanding systems has received increasing research attention.
Bridge the Gap: High-level Semantic Planning for Image Captioning
To bridge the gaps we propose a high-level semantic planning (HSP) mechanism that incorporates both a semantic reconstruction and an explicit order planning.
BYteam at SemEval-2020 Task 5: Detecting Counterfactual Statements with BERT and Ensembles
We participate in the classification tasks of SemEval-2020 Task: Subtask1: Detecting counterfactual statements of semeval-2020 task5(Detecting Counterfactuals).
SparTerm: Learning Term-based Sparse Representation for Fast Text Retrieval
Term-based sparse representations dominate the first-stage text retrieval in industrial applications, due to its advantage in efficiency, interpretability, and exact term matching.
Improving Query Efficiency of Black-box Adversarial Attack
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).
Query Twice: Dual Mixture Attention Meta Learning for Video Summarization
Video summarization aims to select representative frames to retain high-level information, which is usually solved by predicting the segment-wise importance score via a softmax function.
Fatigue Assessment using ECG and Actigraphy Sensors
For deep learning solution, we used state-of-the-art self-attention model, based on which we further proposed a consistency self-attention (CSA) mechanism for fatigue assessment.
Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2
We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Hilbert-Based Generative Defense for Adversarial Examples
Adversarial perturbations of clean images are usually imperceptible for human eyes, but can confidently fool deep neural networks (DNNs) to make incorrect predictions.
Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.
High Energy Physics - Experiment High Energy Physics - Phenomenology
