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Found 90 papers, 51 papers with code
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should not require manual tuning or calibration.
ECG Identification under Exercise and Rest Situations via Various Learning Methods
By applying various existing learning methods to our ECG dataset, we find that current methods which can well support the identification of individuals under rests, do not suffice to present satisfying ECGID performance under exercise situations, therefore exposing the deficiency of existing ECG identification methods.
Raw-to-End Name Entity Recognition in Social Media
Our model neither requires the conversion from character sequences to word sequences, nor assumes tokenizer can correctly detect all word boundaries.
Discriminative Topic Mining via Category-Name Guided Text Embedding
We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.
CrossWeigh: Training Named Entity Tagger from Imperfect Annotations
Therefore, we manually correct these label mistakes and form a cleaner test set.
Ranked #3 on
Named Entity Recognition (NER)
on CoNLL++
(using extra training data)
Learning to Order Sub-questions for Complex Question Answering
Answering complex questions involving multiple entities and relations is a challenging task.
Cross-Lingual Ability of Multilingual BERT: An Empirical Study
Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no aligned data.
Extending Multilingual BERT to Low-Resource Languages
Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning.
Emora: An Inquisitive Social Chatbot Who Cares For You
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
X-Class: Text Classification with Extremely Weak Supervision
Finally, we pick the most confident documents from each cluster to train a text classifier.
Low-Power Wireless Wearable ECG Monitoring Chestbelt Based on Ferroelectric Microprocessor
The ECG monitoring device, abbreviated as ECGM, is designed based on ferroelectric microprocessor which provides ultra-low power consumption and contains four parts-MCU, BLE, Sensors and Power.
"Average" Approximates "First Principal Component"? An Empirical Analysis on Representations from Neural Language Models
In this paper, we present an empirical property of these representations -- "average" approximates "first principal component".
XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction
Motivated by this, we propose a novel Extreme Cross Network, abbreviated XCrossNet, which aims at learning dense and sparse feature interactions in an explicit manner.
Ranked #22 on
Click-Through Rate Prediction
on Criteo
Cross-Domain Contract Element Extraction with a Bi-directional Feedback Clause-Element Relation Network
First, contract elements are far more fine-grained than named entities, which hinders the transfer of extractors.
Cross-Domain Named Entity Recognition
named-entity-recognition
+4
UCPhrase: Unsupervised Context-aware Quality Phrase Tagging
Training a conventional neural tagger based on silver labels usually faces the risk of overfitting phrase surface names.
Ranked #1 on
Phrase Tagging
on KPTimes
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding.
Dual Slot Selector via Local Reliability Verification for Dialogue State Tracking
However, the overwhelming majority of the slots in each turn should simply inherit the slot values from the previous turn.
Membership Inference Attacks Against Recommender Systems
In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.
An Interactive Visualization Tool for Understanding Active Learning
Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency.
Learning from Imperfect Demonstrations via Adversarial Confidence Transfer
Existing learning from demonstration algorithms usually assume access to expert demonstrations.
Weakly Supervised Correspondence Learning
Correspondence learning is a fundamental problem in robotics, which aims to learn a mapping between state, action pairs of agents of different dynamics or embodiments.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy.
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potentials of PLMs can not be fully tapped.
Effectively Using Long and Short Sessions for Multi-Session-based Recommendations
The RNN often used in previous work is not suitable to process short sessions, because RNN only focuses on the sequential relationship, which we find is not the only relationship between items in short sessions.
Beyond the Granularity: Multi-Perspective Dialogue Collaborative Selection for Dialogue State Tracking
However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.
WeDef: Weakly Supervised Backdoor Defense for Text Classification
Inspired by this observation, in WeDef, we define the reliability of samples based on whether the predictions of the weak classifier agree with their labels in the poisoned training set.
Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Fine-tuning pre-trained language models has recently become a common practice in building NLP models for various tasks, especially few-shot tasks.
Rethinking the Setting of Semi-supervised Learning on Graphs
graph benchmark (IGB) consisting of 4 datasets.
SPGNet: Spatial Projection Guided 3D Human Pose Estimation in Low Dimensional Space
We propose a method SPGNet for 3D human pose estimation that mixes multi-dimensional re-projection into supervised learning.
Ranked #46 on
3D Human Pose Estimation
on Human3.6M
Debiasing Learning for Membership Inference Attacks Against Recommender Systems
To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.
CV 3315 Is All You Need : Semantic Segmentation Competition
This competition focus on Urban-Sense Segmentation based on the vehicle camera view.
M^4I: Multi-modal Models Membership Inference
To achieve this, we propose Multi-modal Models Membership Inference (M^4I) with two attack methods to infer the membership status, named metric-based (MB) M^4I and feature-based (FB) M^4I, respectively.
Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations
Specifically, we design a masked policy network with a binary mask to block certain modalities.
WavSpA: Wavelet Space Attention for Boosting Transformers' Long Sequence Learning Ability
Recent works show that learning attention in the Fourier space can improve the long sequence learning capability of Transformers.
GLM-130B: An Open Bilingual Pre-trained Model
We introduce GLM-130B, a bilingual (English and Chinese) pre-trained language model with 130 billion parameters.
Ranked #1 on
Language Modelling
on CLUE (OCNLI_50K)
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search
While Speech Emotion Recognition (SER) is a common application for popular languages, it continues to be a problem for low-resourced languages, i. e., languages with no pretrained speech-to-text recognition models.
Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale
Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health.
Reconstructing Training Data from Model Gradient, Provably
Understanding when and how much a model gradient leaks information about the training sample is an important question in privacy.
Esports Data-to-commentary Generation on Large-scale Data-to-text Dataset
Therefore, in this study, we introduce a task of generating game commentaries from structured data records to address the problem.
Guiding Pretraining in Reinforcement Learning with Large Language Models
Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function.
PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection
Masked Autoencoders learn strong visual representations and achieve state-of-the-art results in several independent modalities, yet very few works have addressed their capabilities in multi-modality settings.
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection
This paper presents our Facial Action Units (AUs) detection submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW).
KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation
The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X
Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer.
Ranked #81 on
Code Generation
on MBPP
Discreetly Exploiting Inter-session Information for Session-based Recommendation
First, inter-session dependencies are not differentiated at the factor-level.
Dual-Granularity Contrastive Learning for Session-based Recommendation
At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data(e. g. factor-level embeddings), with which we can construct factor-level convolution channels.
Iteratively Learning Representations for Unseen Entities with Inter-Rule Correlations
Recent work on knowledge graph completion (KGC) focused on learning embeddings of entities and relations in knowledge graphs.
WOT-Class: Weakly Supervised Open-world Text Classification
State-of-the-art weakly supervised text classification methods, while significantly reduced the required human supervision, still requires the supervision to cover all the classes of interest.
A Benchmark on Extremely Weakly Supervised Text Classification: Reconcile Seed Matching and Prompting Approaches
Etremely Weakly Supervised Text Classification (XWS-TC) refers to text classification based on minimal high-level human guidance, such as a few label-indicative seed words or classification instructions.
Goal-Driven Explainable Clustering via Language Descriptions
We propose a new task formulation, "Goal-Driven Clustering with Explanations" (GoalEx), which represents both the goal and the explanations as free-form language descriptions.
Debiasing Made State-of-the-art: Revisiting the Simple Seed-based Weak Supervision for Text Classification
We show that the limited performance of seed matching is largely due to the label bias injected by the simple seed-match rule, which prevents the classifier from learning reliable confidence for selecting high-quality pseudo-labels.
ClusterLLM: Large Language Models as a Guide for Text Clustering
First, we prompt ChatGPT for insights on clustering perspective by constructing hard triplet questions <does A better correspond to B than C>, where A, B and C are similar data points that belong to different clusters according to small embedder.
Implicit bias of SGD in $L_{2}$-regularized linear DNNs: One-way jumps from high to low rank
The $L_{2}$-regularized loss of Deep Linear Networks (DLNs) with more than one hidden layers has multiple local minima, corresponding to matrices with different ranks.
Deep Neural Networks in Video Human Action Recognition: A Review
Our current related research addresses multiple novel proposed research works and compares their advantages and disadvantages between the derived deep learning frameworks rather than machine learning frameworks.
RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit
To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit.
BPKD: Boundary Privileged Knowledge Distillation For Semantic Segmentation
Current knowledge distillation approaches in semantic segmentation tend to adopt a holistic approach that treats all spatial locations equally.
Breadcrumbs to the Goal: Goal-Conditioned Exploration from Human-in-the-Loop Feedback
This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.
GridMM: Grid Memory Map for Vision-and-Language Navigation
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments.
MINT: Evaluating LLMs in Multi-turn Interaction with Tools and Language Feedback
However, current evaluation protocols often emphasize benchmark performance with single-turn exchanges, neglecting the nuanced interactions among the user, LLMs, and external tools, while also underestimating the importance of natural language feedback from users.
Misusing Tools in Large Language Models With Visual Adversarial Examples
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities.
Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models
Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.
Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing
In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback.
ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation
This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content.
Autonomous Robotic Reinforcement Learning with Asynchronous Human Feedback
We evaluate this system on a suite of robotic tasks in simulation and demonstrate its effectiveness at learning behaviors both in simulation and the real world.
EmojiLM: Modeling the New Emoji Language
With the rapid development of the internet, online social media welcomes people with different backgrounds through its diverse content.
Less than One-shot: Named Entity Recognition via Extremely Weak Supervision
We study the named entity recognition (NER) problem under the extremely weak supervision (XWS) setting, where only one example entity per type is given in a context-free way.
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e. g., law articles, charges, and terms of penalty) for single-defendant cases.
CogAgent: A Visual Language Model for GUI Agents
People are spending an enormous amount of time on digital devices through graphical user interfaces (GUIs), e. g., computer or smartphone screens.
Ranked #14 on
Visual Question Answering
on MM-Vet
A Challenger to GPT-4V? Early Explorations of Gemini in Visual Expertise
They endow Large Language Models (LLMs) with powerful capabilities in visual understanding, enabling them to tackle diverse multi-modal tasks.
TeleChat Technical Report
Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe.
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization
Semi-supervised learning (SSL), thanks to the significant reduction of data annotation costs, has been an active research topic for large-scale 3D scene understanding.
SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning
To bridge these gaps, we introduce SciGLM, a suite of scientific language models able to conduct college-level scientific reasoning.
Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision
Process supervision, using a trained verifier to evaluate the intermediate steps generated by reasoner, has demonstrated significant improvements in multi-step problem solving.
Data Reconstruction Attacks and Defenses: A Systematic Evaluation
In this work, we propose a strong reconstruction attack in the setting of federated learning.
Answer is All You Need: Instruction-following Text Embedding via Answering the Question
This work aims to build a text embedder that can capture characteristics of texts specified by user instructions.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure.
Learning with Noisy Foundation Models
Foundation models are usually pre-trained on large-scale datasets and then adapted to downstream tasks through tuning.
Towards Robustness and Diversity: Continual Learning in Dialog Generation with Text-Mixup and Batch Nuclear-Norm Maximization
In our dynamic world where data arrives in a continuous stream, continual learning enables us to incrementally add new tasks/domains without the need to retrain from scratch.
Is Mamba Effective for Time Series Forecasting?
Furthermore, we conduct extensive experiments to delve deeper into the potential of Mamba compared to the Transformer in the TSF.
MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks
We construct the distillation dataset via sampling sentences from language model pre-training datasets (e. g., OpenWebText in our implementation) and prompting an LLM to identify the typed spans of "important information".
Lookahead Exploration with Neural Radiance Representation for Continuous Vision-Language Navigation
Vision-and-language navigation (VLN) enables the agent to navigate to a remote location following the natural language instruction in 3D environments.
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline
Large language models (LLMs) have shown excellent mastering of human language, but still struggle in real-world applications that require mathematical problem-solving.
AirShot: Efficient Few-Shot Detection for Autonomous Exploration
Few-shot object detection has drawn increasing attention in the field of robotic exploration, where robots are required to find unseen objects with a few online provided examples.
Multi-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition
Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions.
Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving
Recent advances in Automated Theorem Proving have shown the effectiveness of leveraging a (large) language model that generates tactics (i. e. proof steps) to search through proof states.
Tele-FLM Technical Report
Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications.
“Average” Approximates “First Principal Component”? An Empirical Analysis on Representations from Neural Language Models
In this paper, we present an empirical property of these representations—”average” approximates “first principal component”.
Learning Adaptive Axis Attentions in Fine-tuning: Beyond Fixed Sparse Attention Patterns
We present a comprehensive study of sparse attention patterns in Transformer models.
