A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
MAGNA improves graph neural networks by incorporating multi-hop context information.
FinReflectKG benchmarks financial QA by linking relevant context from a financial KG, improving model performance and efficiency.
A novel beam training scheme optimizes multi-hop THz communications with up to 75% performance gain.
Method integrates logical rules into neural multi-hop reasoning for drug repurposing.
We present efficient differentiable implementations of second-order multi-hop reasoning using a large symbolic knowledge base (KB). We introduce a new operation which can be used to compositionally construct second-order multi-hop templates in a neural model, and evaluate a number of alternative implementations, with d…
Recent breakthroughs in computer vision and natural language processing have spurred interest in challenging multi-modal tasks such as visual question-answering and visual dialogue. For such tasks, one successful approach is to condition image-based convolutional network computation on language via Feature-wise Linear …
Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the resear…
On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity. However, for privacy guarantee, it is inevitable at each device to compensate for the quality of data or learning performanc…
RDLI integrates domain logic and context grounding to detect crypto anomalies under scarce labels.
New neural KB representation speeds up reasoning with large symbolic knowledge bases.
Graph neural networks improve volatility forecasting by capturing spillover effects.
Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic f…
This study compares GNNs and GA-MLPs, finding GA-MLPs can distinguish graphs but not count walks.
This work investigates how multi-round reasoning improves LLM performance.
Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that l…
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood relations in CF directly to address the sparsity problem. In this paper, we propose superhighway const…
ManifoldMind uses adaptive-curvature probabilistic spheres for trustworthy recommendations in semantic hierarchies.
End-to-end KBQA system learns from multiple reasoning paths without labeled paths.
CorePPR combines PageRank and CoreRank for scalable GNNs.
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label predictions are made by jointly modeling the node and its' neighborhood features. State-of-the-art models for node classification on such attrib…
DRew dynamically rewires message passing to improve long-range tasks.
Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of …
Proposes a graph pooling method leveraging node proximity for hierarchical graph representation learning.
Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction. However, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs. The limitations especial…
Graph Cascades rewire graphs to improve structure-aware learning.
UniFinEval benchmarks financial models across text, images, and videos.
Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and …
Real-time traffic volume inference is key to an intelligent city. It is a challenging task because accurate traffic volumes on the roads can only be measured at certain locations where sensors are installed. Moreover, the traffic evolves over time due to the influences of weather, events, holidays, etc. Existing soluti…
Neuro-symbolic system tackles conversational AI's need for natural, broad-ranging dialogue.
Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more complicated than in Euclidian space, recent studies have extensively utilized neural nets to effectively and efficiently embed a graph's no…
With the arrival of the big data era, more and more data are becoming readily available in various real-world applications and those data are usually highly heterogeneous. Taking computational medicine as an example, we have both Electronic Health Records (EHR) and medical images for each patient. For complicated disea…
Privacy-preserving GNNs for graph data with sensitive node data.
MPNNs struggle with class-bottlenecks and heterophily, leading to performance limitations.
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning labels to every unlabeled node using information from the node as well as its neighbors. It is often the case that a node is not only influenc…
A new method for MARL with partial observations reduces communication overhead.
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling …
Enhances activity recognition in wearable computing with context awareness and uncertainty quantification.
MLPs can approximate any function in context, challenging the importance of in-context universality.
Paper argues context equals environment, improving AI generalization.
Scales attention for long contexts in LLMs.
Enhances neural processes to learn from multiple related datasets.
Transformers can scale both context and task, but MLPs can only scale task.
We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution. The learner observes only the context distribution while the exact context realization remains hidden. This allows for a broad range of appl…
Transformers can be hijacked by context, but deeper models are more robust.
This study examines how sequential correlations affect in-context learning in sequence models.
New method for contextual bandits with corrupted context.
Paper proposes linear transformers for efficient in-context learning without context length limitations.
Mobile context determination is an important step for many context aware services such as location-based services, enterprise policy enforcement, building or room occupancy detection for power or HVAC operation, etc. Especially in enterprise scenarios where policies (e.g., attending a confidential meeting only when the…