Papers RTE
“RTE” 태그가 달린 논문 111편 · 필터 해제
DeepRTE: Pre-trained Attention-based Neural Network for Radiative Tranfer
In this study, we propose a novel neural network approach, termed DeepRTE, to address the steady-state Radiative Transfer Equation (RTE). The RTE is a differential-integral equation that governs the propagation of radiat…
Computational EfficiencyRTEUnsupervised Clustering for Fault Analysis in High-Voltage Power Systems Using Voltage and Current Signals
The widespread use of sensors in modern power grids has led to the accumulation of large amounts of voltage and current waveform data, especially during fault events. However, the lack of labeled datasets poses a signifi…
Fault DiagnosisRTETime Series ClusteringPerson Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait
We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and Ran…
Face RecognitionPerson RecognitionRTETAR+1SMARTe: Slot-based Method for Accountable Relational Triple extraction
Relational Triple Extraction (RTE) is a fundamental task in Natural Language Processing (NLP). However, prior research has primarily focused on optimizing model performance, with limited efforts to understand the interna…
RTERL2Grid: Benchmarking Reinforcement Learning in Power Grid Operations
Reinforcement learning (RL) can transform power grid operations by providing adaptive and scalable controllers essential for grid decarbonization. However, existing methods struggle with the complex dynamics, aleatoric u…
Benchmarkingreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1ESNLIR: A Spanish Multi-Genre Dataset with Causal Relationships
Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), serves as a crucial area within the domain of Natural Language Processing (NLP). This area fundamentally empowers machines to discern …
Natural Language InferenceRTEPhysics-informed neural networks for modeling atmospheric radiative transfer
Understanding the radiative transfer processes in the Earth’s atmosphere is crucial for accurate climate modeling and climate change predictions. These processes are governed by complex physical phenomena, which can be g…
RTEReconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry
Deep learning's preponderance across scientific domains has reshaped high-stakes decision-making, making it essential to follow rigorous operational frameworks that include both Right-to-Privacy (RTP) and Right-to-Explan…
Decision MakingPrivacy PreservingRTEPrice impact and long-term profitability of energy storage
We study the price impact of storage facilities in electricity markets and analyze the long-term profitability of these facilities in prospective scenarios of energy transition. To this end, we begin by characterizing th…
RTEIn-context KV-Cache Eviction for LLMs via Attention-Gate
The KV-Cache technique has become the standard for the inference of large language models (LLMs). Yet, it is widely criticized that KV-Cache can become a bottleneck of the LLM inference system. This paper enables a novel…
RTELogicPro: Improving Complex Logical Reasoning via Program-Guided Learning
In this paper, we present a novel approach, called LogicPro, to enhance Large Language Models (LLMs) complex Logical reasoning through Program Examples. We do this effectively by simply utilizing widely available algorit…
GSM8KLogical ReasoningRTEDSLO: Deep Sequence LiDAR Odometry Based on Inconsistent Spatio-temporal Propagation
This paper introduces a 3D point cloud sequence learning model based on inconsistent spatio-temporal propagation for LiDAR odometry, termed DSLO. It consists of a pyramid structure with a spatial information reuse strate…
RTEADMM Based Semi-Structured Pattern Pruning Framework For Transformer
NLP(natural language processsing) has achieved great success through the transformer model.However, the model has hundreds of millions or billions parameters,which is huge burden for its deployment on personal computer o…
CoLAQuantizationRTEptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language
Despite advancements in Natural Language Processing (NLP) and the growing availability of pretrained models, the English language remains the primary focus of model development. Continued pretraining on language-specific…
RTESTSNew Datasets for Automatic Detection of Textual Entailment and of Contradictions between Sentences in French
This paper introduces DACCORD, an original dataset in French for automatic detection of contradictions between sentences. It also presents new, manually translated versions of two datasets, namely the well known dataset …
Natural Language InferenceRTESentenceSentence-Pair Classification+1Identification of Entailment and Contradiction Relations between Natural Language Sentences: A Neurosymbolic Approach
Natural language inference (NLI), also known as Recognizing Textual Entailment (RTE), is an important aspect of natural language understanding. Most research now uses machine learning and deep learning to perform this ta…
Abstract Meaning RepresentationNatural Language InferenceNatural Language UnderstandingRTEWhen does word order matter and when doesn't it?
Language models (LMs) may appear insensitive to word order changes in natural language understanding (NLU) tasks. In this paper, we propose that linguistic redundancy can explain this phenomenon, whereby word order and o…
Natural Language UnderstandingRTESST-2Sparse MeZO: Less Parameters for Better Performance in Zeroth-Order LLM Fine-Tuning
While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-o…
GPUparameter-efficient fine-tuningRTEUnlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompts and (2) how to select proper demonstra…
BlockingIn-Context LearningRTEUnlocking the Potential: Multi-task Deep Learning for Spaceborne Quantitative Monitoring of Fugitive Methane Plumes
As global warming intensifies, increased attention is being paid to monitoring fugitive methane emissions and detecting gas plumes from landfills. We have divided methane emission monitoring into three subtasks: methane …
Multi-Task LearningRTESegmentation