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Found 14 papers, 4 papers with code
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making.
Boosting Urban Traffic Speed Prediction via Integrating Implicit Spatial Correlations
To tackle the challenge, we propose a generic model for enabling the current traffic speed prediction methods to preserve implicit spatial correlations.
Human-instructed Deep Hierarchical Generative Learning for Automated Urban Planning
The third stage is to leverage multi-attentions to model the zone-zone peer dependencies of the functionality projections to generate grid-level land-use configurations.
Graph Soft-Contrastive Learning via Neighborhood Ranking
Moreover, as the neighborhood size exponentially expands with more hops considered, we propose neighborhood sampling strategies to improve learning efficiency.
Adversarial Attacks on ASR Systems: An Overview
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Learning to Walk with Dual Agents for Knowledge Graph Reasoning
Graph walking based on reinforcement learning (RL) has shown great success in navigating an agent to automatically complete various reasoning tasks over an incomplete knowledge graph (KG) by exploring multi-hop relational paths.
Domain-oriented Language Pre-training with Adaptive Hybrid Masking and Optimal Transport Alignment
Also, the word co-occurrences guided semantic learning of pre-training models can be largely augmented by entity-level association knowledge.
RPT: Toward Transferable Model on Heterogeneous Researcher Data via Pre-Training
In this paper, we propose a multi-task self-supervised learning-based researcher data pre-training model named RPT.
Learning Disentangled Representations for Time Series
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.
T$^2$-Net: A Semi-supervised Deep Model for Turbulence Forecasting
Accurate air turbulence forecasting can help airlines avoid hazardous turbulence, guide the routes that keep passengers safe, maximize efficiency, and reduce costs.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e. g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns.
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce
Pre-trained language models such as BERT have achieved great success in a broad range of natural language processing tasks.
Path-Based Attention Neural Model for Fine-Grained Entity Typing
Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure.
Efficient Parallel Translating Embedding For Knowledge Graphs
Knowledge graph embedding aims to embed entities and relations of knowledge graphs into low-dimensional vector spaces.
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