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Found 38 papers, 16 papers with code
The NiuTrans System for the WMT20 Quality Estimation Shared Task
This paper describes the submissions of the NiuTrans Team to the WMT 2020 Quality Estimation Shared Task.
A Novel Joint Framework for Multiple Chinese Events Extraction
The task of argument extraction is converted to event relation triple extraction.
Kimi-VL Technical Report
We present Kimi-VL, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers advanced multimodal reasoning, long-context understanding, and strong agent capabilities - all while activating only 2. 8B parameters in its language decoder (Kimi-VL-A3B).
Falcon: A Remote Sensing Vision-Language Foundation Model
This paper introduces a holistic vision-language foundation model tailored for remote sensing, named Falcon.
How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching
Legal case retrieval (LCR) aims to automatically scour for comparable legal cases based on a given query, which is crucial for offering relevant precedents to support the judgment in intelligent legal systems.
CHBench: A Chinese Dataset for Evaluating Health in Large Language Models
With the rapid development of large language models (LLMs), assessing their performance on health-related inquiries has become increasingly essential.
Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge
On the other hand, D-LADAN presents a novel momentum-updated memory mechanism to dynamically sense the posterior similarity between law articles (or charges) and a weighted GDO to adaptively capture the distinctions for revising the inductive bias caused by the data imbalance problem.
Unified End-to-End V2X Cooperative Autonomous Driving
V2X cooperation, through the integration of sensor data from both vehicles and infrastructure, is considered a pivotal approach to advancing autonomous driving technology.
Garbage Segmentation and Attribute Analysis by Robotic Dogs
In this study, we propose GSA2Seg (Garbage Segmentation and Attribute Analysis), a novel visual approach that utilizes quadruped robotic dogs as autonomous agents to address waste management and recycling challenges in diverse indoor and outdoor environments.
Android in the Zoo: Chain-of-Action-Thought for GUI Agents
To address this, this work presents Chain-of-Action-Thought (dubbed CoAT), which takes the description of the previous actions, the current screen, and more importantly the action thinking of what actions should be performed and the outcomes led by the chosen action.
Aligning Knowledge Graph with Visual Perception for Object-goal Navigation
Object-goal navigation is a challenging task that requires guiding an agent to specific objects based on first-person visual observations.
LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition
To overcome the limitations of existing data augmentation methods that compromise semantic integrity and address the uncertainty inherent in LLM-generated text, we leverage the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
Advancing Translation Preference Modeling with RLHF: A Step Towards Cost-Effective Solution
To address this issue, we propose a cost-effective preference learning strategy, optimizing reward models by distinguishing between human and machine translations.
Secrets of RLHF in Large Language Models Part II: Reward Modeling
We introduce a series of novel methods to mitigate the influence of incorrect and ambiguous preferences in the dataset and fully leverage high-quality preference data.
Disordered hyperuniformity signals functioning and resilience of self-organized vegetation patterns
Here we show that disordered hyperuniformity as a striking class of hidden orders can exist in spatially self-organized vegetation landscapes.
DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF
Thus, we propose DocStormer, a novel algorithm designed to restore multi-degraded colored documents to their potential pristine PDF.
Spectral-DP: Differentially Private Deep Learning through Spectral Perturbation and Filtering
In particular, for fully connected layers, we combine a block-circulant based spatial restructuring with Spectral-DP to achieve better utility.
Secrets of RLHF in Large Language Models Part I: PPO
Therefore, we explore the PPO-max, an advanced version of PPO algorithm, to efficiently improve the training stability of the policy model.
MERGE: Fast Private Text Generation
The drastic increase in language models' parameters has led to a new trend of deploying models in cloud servers, raising growing concerns about private inference for Transformer-based models.
Ranked #1 on
Multi-task Language Understanding
on MMLU (5-Shot)
(using extra training data)
PARAGRAPH2GRAPH: A GNN-based framework for layout paragraph analysis
Document layout analysis has a wide range of requirements across various domains, languages, and business scenarios.
Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Experimental results show that NDSNN achieves up to 20. 52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e. g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN).
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities.
How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
The GPT-3. 5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities.
Natural Language Inference
Natural Language Understanding
+1
RRNet: Towards ReLU-Reduced Neural Network for Two-party Computation Based Private Inference
The proliferation of deep learning (DL) has led to the emergence of privacy and security concerns.
CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference
To this end, we develop an approach that can effectively take advantage of the sparsity of matrix operations in GCN inference to significantly reduce the computational overhead.
PolyMPCNet: Towards ReLU-free Neural Architecture Search in Two-party Computation Based Private Inference
The rapid growth and deployment of deep learning (DL) has witnessed emerging privacy and security concerns.
Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
First, we show that successful white-box adversarial attacks on SNNs are highly dependent on the underlying surrogate gradient technique, even in the case of adversarially trained SNNs.
NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks
Membership inference attacks (MIAs) against machine learning models can lead to serious privacy risks for the training dataset used in the model training.
Measurement of carbon finance level and exploration of its influencing factors
Faced with increasingly severe environmental problems, carbon trading markets and related financial activities aiming at limiting carbon dioxide emissions are booming.
AME: Attention and Memory Enhancement in Hyper-Parameter Optimization
Training Deep Neural Networks (DNNs) is inherently subject to sensitive hyper-parameters and untimely feedbacks of performance evaluation.
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection.
Learning to Check Contract Inconsistencies
This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract.
Learning Architectures from an Extended Search Space for Language Modeling
Neural architecture search (NAS) has advanced significantly in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell.
Distinguish Confusing Law Articles for Legal Judgment Prediction
Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts, which has excellent prospects in judicial assistance systems and convenient services for the public.
The NiuTrans Machine Translation Systems for WMT19
We participated in 13 translation directions, including 11 supervised tasks, namely EN↔{ZH, DE, RU, KK, LT}, GU→EN and the unsupervised DE↔CS sub-track.
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
To resolve these problems, we present MR-GNN, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (L-STMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs.
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recently.
GP-Localize: Persistent Mobile Robot Localization using Online Sparse Gaussian Process Observation Model
Central to robot exploration and mapping is the task of persistent localization in environmental fields characterized by spatially correlated measurements.
