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Found 14 papers, 5 papers with code
Seed1.5-Thinking: Advancing Superb Reasoning Models with Reinforcement Learning
We introduce Seed1. 5-Thinking, capable of reasoning through thinking before responding, resulting in improved performance on a wide range of benchmarks.
Predictive Lagrangian Optimization for Constrained Reinforcement Learning
And we prove that the resulting optimal policy, achieved through alternating MFOCP and MGPL, aligns with the solution of the primal constrained RL problem, thereby establishing our equivalence framework.
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning
AI becomes increasingly vital for telecom industry, as the burgeoning complexity of upcoming mobile communication networks places immense pressure on network operators.
Spatial-Temporal Attention Model for Traffic State Estimation with Sparse Internet of Vehicles
The growing number of connected vehicles offers an opportunity to leverage internet of vehicles (IoV) data for traffic state estimation (TSE) which plays a crucial role in intelligent transportation systems (ITS).
SBoRA: Low-Rank Adaptation with Regional Weight Updates
This paper introduces Standard Basis LoRA (SBoRA), a novel parameter-efficient fine-tuning approach for Large Language Models that builds upon the pioneering works of Low-Rank Adaptation (LoRA) and Orthogonal Adaptation.
Coverage Analysis of Downlink Transmission in Multi-Connectivity Cellular V2X Networks
To this end, this paper proposes a framework for analyzing the performance of multi-connectivity in C-V2X downlink transmission, with a focus on the performance indicators of joint distance distribution and coverage probability.
Performance Analysis for Downlink Transmission in Multi-Connectivity Cellular V2X Networks
With the ever-increasing number of connected vehicles in the fifth-generation mobile communication networks (5G) and beyond 5G (B5G), ensuring the reliability and high-speed demand of cellular vehicle-to-everything (C-V2X) communication in scenarios where vehicles are moving at high speeds poses a significant challenge. Recently, multi-connectivity technology has become a promising network access paradigm for improving network performance and reliability for C-V2X in the 5G and B5G era.
The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly.
SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering
This approach markedly improves clustering speed and efficiency, serving as a catalyst for real-time, high-throughput data analysis in future healthcare applications.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
The uplink (UL)/downlink (DL) decoupled access has been emerging as a novel access architecture to improve the performance gains in cellular networks.
Spectral Efficiency Analysis of Uplink-Downlink Decoupled Access in C-V2X Networks
VL-CheckList: Evaluating Pre-trained Vision-Language Models with Objects, Attributes and Relations
Inspired by the CheckList for testing natural language processing, we exploit VL-CheckList, a novel framework to understand the capabilities of VLP models.
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver.
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.
Option Pricing Under a Discrete-Time Markov Switching Stochastic Volatility with Co-Jump Model
We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility.
