Paper
Solving Constrained Mean-Variance Portfolio Optimization Using Spiral Optimization
Apply Spiral Optimization Algorithm (SOA) to mean-variance portfolio problems with buy-in thresholds and cardinality constraints. Covers MINLP formulation, penalty methods, and performance comparison.
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Prefix-Tuning adapts frozen LLMs by learning continuous key/value vectors injected into attention. Covers the method, reparameterization, KV-cache mechanics, and comparisons with prompt tuning, adapters, and LoRA.
MoSLoRA: Mixture-of-Subspaces in Low-Rank Adaptation
MoSLoRA boosts LoRA expressivity by mixing multiple low-rank subspaces with a lightweight mixer. Covers when vanilla LoRA fails, mixer design choices, and tuning tips.
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
HCGR embeds session graphs in the Lorentz model of hyperbolic space and trains them with InfoNCE-style contrastive learning. This review unpacks why hierarchical session intent fits hyperbolic geometry, how Lorentz …
paper2repo: GitHub Repository Recommendation for Academic Papers
paper2repo aligns academic papers with GitHub repositories in a shared embedding space using a constrained GCN. Covers the joint heterogeneous graph, the WARP ranking loss, the cosine alignment constraint, and the full …
Session-based Recommendation with Graph Neural Networks (SR-GNN)
SR-GNN turns a click session into a directed weighted graph and runs a gated GNN to predict the next item. Covers session-graph construction, GGNN updates, attention-based session pooling, training, benchmarks, and the …
Graph Contextualized Self-Attention Network (GC-SAN) for Session-based Recommendation
GC-SAN combines a session-graph GGNN (local transitions) with multi-layer self-attention (global dependencies) for session-based recommendation. Covers graph construction, message passing, attention fusion, and where the …
LLMGR: Integrating Large Language Models with Graphical Session-Based Recommendation
LLMGR uses an LLM as the semantic engine for session-based recommendation and a GNN as the ranker. Covers the hybrid encoding layer, two-stage prompt tuning, ~8.68% HR@20 lift, and how to deploy without running an LLM …
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Represent a neural network as a directed graph (neurons as nodes, weights as edges) and use a GNN to produce permutation-equivariant embeddings. The right symmetry unlocks generalisation prediction, network …