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Found 24 papers, 9 papers with code
Paraphrase Generation with Deep Reinforcement Learning
The generator, built as a sequence-to-sequence learning model, can produce paraphrases given a sentence.
2018 Low-Power Image Recognition Challenge
The Low-Power Image Recognition Challenge (LPIRC, https://rebootingcomputing. ieee. org/lpirc) is an annual competition started in 2015.
Low-Power Computer Vision: Status, Challenges, Opportunities
In addition to mobile phones, many autonomous systems rely on visual data for making decisions and some of these systems have limited energy (such as unmanned aerial vehicles also called drones and mobile robots).
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing
We present the first sentence simplification model that learns explicit edit operations (ADD, DELETE, and KEEP) via a neural programmer-interpreter approach.
Ranked #2 on
Text Simplification
on PWKP / WikiSmall
(SARI metric)
Decomposable Neural Paraphrase Generation
Paraphrasing exists at different granularity levels, such as lexical level, phrasal level and sentential level.
Unsupervised Text Generation by Learning from Search
In this work, we present TGLS, a novel framework to unsupervised Text Generation by Learning from Search.
Biclustering with Alternating K-Means
Biclustering is the task of simultaneously clustering the rows and columns of the data matrix into different subgroups such that the rows and columns within a subgroup exhibit similar patterns.
Overfitting or Underfitting? Understand Robustness Drop in Adversarial Training
Our goal is to understand why the robustness drops after conducting adversarial training for too long.
Can Shape Structure Features Improve Model Robustness Under Diverse Adversarial Settings?
Specifically, EdgeNetRob and EdgeGANRob first explicitly extract shape structure features from a given image via an edge detection algorithm.
Classification with Nearest Disjoint Centroids
In this paper, we develop a new classification method based on nearest centroid, and it is called the nearest disjoint centroid classifier.
BFClass: A Backdoor-free Text Classification Framework
To recognize the poisoned subset, we examine the training samples with these identified triggers as the most suspicious token, and check if removing the trigger will change the poisoned model's prediction.
Perturbation Deterioration: The Other Side of Catastrophic Overfitting
While this phenomenon is commonly explained as overfitting, we observe that it is a twin process: not only does the model catastrophic overfits to one type of perturbation, but also the perturbation deteriorates into random noise.
Beam Search for Feature Selection
In this paper, we present and prove some consistency results about the performance of classification models using a subset of features.
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
We train a neural model with this feedback data that can generate explanations and re-score answer candidates.
Ranked #1 on
Overall - Test
on FeedbackQA
Text Revision by On-the-Fly Representation Optimization
In this approach, we simply fine-tune a pre-trained Transformer with masked language modeling and attribute classification.
Bag of Tricks for FGSM Adversarial Training
Adversarial training (AT) with samples generated by Fast Gradient Sign Method (FGSM), also known as FGSM-AT, is a computationally simple method to train robust networks.
On the Adversarial Robustness of Camera-based 3D Object Detection
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost.
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
For example, even the simple flips-and-crops augmentation requires training for more than 5 epochs to improve performance, whereas RandAugment requires more than 90 epochs.
f-Divergence Minimization for Sequence-Level Knowledge Distillation
Experiments across four datasets show that our methods outperform existing KD approaches, and that our symmetric distilling losses can better force the student to learn from the teacher distribution.
Evaluating Dependencies in Fact Editing for Language Models: Specificity and Implication Awareness
The potential of using a large language model (LLM) as a knowledge base (KB) has sparked significant interest.
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
With regards to data, we demonstrate the significance of high-quality training data and show that a smaller dataset of high-quality data can outperform a larger dataset with lower quality.
Towards Adaptive Residual Network Training: A Neural-ODE Perspective
Serving as a crucial factor, the depth of residual networks balances model capacity, performance, and training efficiency.
Codewithzichao@DravidianLangTech-EACL2021: Exploring Multilingual Transformers for Offensive Language Identification on Code Mixing Text
This paper describes our solution submitted to shared task on Offensive Language Identification in Dravidian Languages.
Codewithzichao@DravidianLangTech-EACL2021: Exploring Multimodal Transformers for Meme Classification in Tamil Language
This paper describes our submission to shared task on Meme Classification for Tamil Language.
