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Found 28 papers, 13 papers with code
Large Language Model Safety: A Holistic Survey
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation.
DA-MoE: Addressing Depth-Sensitivity in Graph-Level Analysis through Mixture of Experts
Empirically, fewer layers are sufficient for message passing in smaller graphs, while larger graphs typically require deeper networks to capture long-range dependencies and global features.
Hi-GMAE: Hierarchical Graph Masked Autoencoders
To ensure masking uniformity of subgraphs across these scales, we propose a novel coarse-to-fine strategy that initiates masking at the coarsest scale and progressively back-projects the mask to the finer scales.
Gradformer: Graph Transformer with Exponential Decay
Therefore, this paper presents Gradformer, a method innovatively integrating GT with the intrinsic inductive bias by applying an exponential decay mask to the attention matrix.
Where to Mask: Structure-Guided Masking for Graph Masked Autoencoders
To this end, we introduce a novel structure-guided masking strategy (i. e., StructMAE), designed to refine the existing GMAE models.
LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Current datasets collect questions from Chinese examinations across different subjects and educational levels to address this issue.
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety
In addition to these benchmarks, we have implemented a phased public evaluation and benchmark update strategy to ensure that OpenEval is in line with the development of Chinese LLMs or even able to provide cutting-edge benchmark datasets to guide the development of Chinese LLMs.
Exploring Sparsity in Graph Transformers
Graph Transformers (GTs) have achieved impressive results on various graph-related tasks.
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes
Graph pooling has been increasingly recognized as crucial for Graph Neural Networks (GNNs) to facilitate hierarchical graph representation learning.
Evaluating Large Language Models: A Comprehensive Survey
We hope that this comprehensive overview will stimulate further research interests in the evaluation of LLMs, with the ultimate goal of making evaluation serve as a cornerstone in guiding the responsible development of LLMs.
Large Language Model Alignment: A Survey
We also envision bridging the gap between the AI alignment research community and the researchers engrossed in the capability exploration of LLMs for both capable and safe LLMs.
Dynamical Isometry based Rigorous Fair Neural Architecture Search
In addition, due to the negligence of fairness, current methods are prone to make misjudgments in module evaluation.
On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling
A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology.
M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models
Comprehensively evaluating the capability of large language models in multiple tasks is of great importance.
Empirical Evaluation of ChatGPT on Requirements Information Retrieval Under Zero-Shot Setting
Our evaluation of ChatGPT on requirements IR under zero-shot setting provides preliminary evidence for designing or developing more effective requirements IR methods or tools based on LLMs.
Indeterminate Probability Neural Network
Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced.
Data-driven design of fault diagnosis for three-phase PWM rectifier using random forests technique with transient synthetic features
A three-phase pulse-width modulation (PWM) rectifier can usually maintain operation when open-circuit faults occur in insulated-gate bipolar transistors (IGBTs), which will lead the system to be unstable and unsafe.
Fault diagnosis for open-circuit faults in NPC inverter based on knowledge-driven and data-driven approaches
Finally, the diagnosis results of online fault diagnosis experiments show that the proposed classifier can locate the open-circuit fault of IGBTs in NPC inverter under the conditions of different loads.
Fault Diagnosis for Power Electronics Converters based on Deep Feedforward Network and Wavelet Compression
Secondly, the wavelet transform is used to remove the redundant data of the features, and then the training sample data is greatly compressed.
Review for AI-based Open-Circuit Faults Diagnosis Methods in Power Electronics Converters
Power electronics converters have been widely used in aerospace system, DC transmission, distributed energy, smart grid and so forth, and the reliability of power electronics converters has been a hotspot in academia and industry.
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks
However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists.
Using EBGAN for Anomaly Intrusion Detection
The generator in IDS-EBGAN is responsible for converting the original malicious network traffic in the training set into adversarial malicious examples.
Multi-objective Pointer Network for Combinatorial Optimization
Recently, a number of deep reinforcement learning (DRL) methods have been proposed to generate approximate optimal solutions to the combinatorial optimization problems.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation.
Subtask-dominated Transfer Learning for Long-tail Person Search
One major challenge comes from the imbalanced long-tail person identity distributions, which prevents the one-step person search model from learning discriminative person features for the final re-identification.
Making Person Search Enjoy the Merits of Person Re-identification
In the proposed TDN, for better knowledge transfer from the Re-ID teacher model to the one-step person search model, we design a strong one-step person search base framework by partially disentangling the two subtasks.
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks
Link and sign prediction in complex networks bring great help to decision-making and recommender systems, such as in predicting potential relationships or relative status levels.
