arXiv research

Optimizes query routing to LLMs under cost and resource constraints.

Recently similarity graphs became the leading paradigm for efficient nearest neighbor search, outperforming traditional tree-based and LSH-based methods. Similarity graphs perform the search via greedy routing: a query traverses the graph and in each vertex moves to the adjacent vertex that is the closest to this query…

CARROT optimizes LLM routing by choosing the cheapest and most accurate model.

New algorithm tackles dynamic query routing to multiple embedding models.

The DEBS Grand Challenge 2018 is set in the context of maritime route prediction. Vessel routes are modeled as streams of Automatic Identification System (AIS) data points selected from real-world tracking data. The challenge requires to correctly estimate the destination ports and arrival times of vessel trips, as ear…

MICO uses mutual information co-training to improve selective search efficiency.

End-to-end framework learns LLM routing from observational data.

Polestar optimizes public transportation routes for efficiency and user satisfaction.

A new method routes EEG covariance matrices across domains using adaptive subspace selection.

New analysis shows how cross-entropy training shapes attention in transformers.

Meta-Router optimizes LLM selection using gold-standard and preference-based data.

Mixtures of neural operators reduce active complexity in operator learning.

Efficient sparse attention reduces self-attention complexity and improves model performance.

A new method for efficient neural network fine-tuning using queryable low-rank update atoms.

New algorithm learns halfspaces almost optimally with fewer queries.

Amortizes MIPS by training neural networks to predict optimal keys.

Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…

JAMPR learns to solve complex VRP with time windows.

We introduce a new routing algorithm for capsule networks, in which a child capsule is routed to a parent based only on agreement between the parent's state and the child's vote. The new mechanism 1) designs routing via inverted dot-product attention; 2) imposes Layer Normalization as normalization; and 3) replaces seq…

A large GPS dataset reveals that a single route often covers 60% of travel observations.

Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with wires without violating the design rules for the printed circuit boards or integrated circuits. Similar routing problems also exist in the de…

We propose and systematically evaluate three strategies for training dynamically-routed artificial neural networks: graphs of learned transformations through which different input signals may take different paths. Though some approaches have advantages over others, the resulting networks are often qualitatively similar…

A new router uses attention-based reinforcement learning to solve detailed routing problems efficiently.

Quantum computing speeds up option pricing for multiple assets.

Unified routing and arbitrage with concave continuation.

A new approach integrates inventory prediction and routing optimization for better supply chain management.

Sparse routing networks with co-training prevent catastrophic forgetting in continual learning.

A recently proposed method in deep learning groups multiple neurons to capsules such that each capsule represents an object or part of an object. Routing algorithms route the output of capsules from lower-level layers to upper-level layers. In this paper, we prove that state-of-the-art routing procedures decrease the e…

Capsules are the multidimensional analogue to scalar neurons in neural networks, and because they are multidimensional, much more complex routing schemes can be used to pass information forward through the network than what can be used in traditional neural networks. This work treats capsules as collections of neurons …

Capsule networks improve at detecting changes in compositionality with routing.

SOCP uses SOM to find groups and local calibration buffers for better regional coverage.

Enhanced route planning with probabilistic prediction and uncertainty sets.

New machine learning pipeline solves dynamic vehicle routing problems efficiently.

New algorithm for traffic routing in congested conditions.

Optimal crypto asset routing with CFMMs, including fixed costs.

DPDP combines neural heuristics with DP for vehicle routing problems.

Framework improves ETF volatility forecasting by adapting to market conditions.

High-level synthesis (HLS) shortens the development time of hardware designs and enables faster design space exploration at a higher abstraction level. Optimization of complex applications in HLS is challenging due to the effects of implementation issues such as routing congestion. Routing congestion estimation is abse…

World wide road traffic fatality and accident rates are high, and this is true even in technologically advanced countries like the USA. Despite the advances in Intelligent Transportation Systems, safe transportation routing i.e., finding safest routes is largely an overlooked paradigm. In recent years, large amount of …

Equity-Transformer solves NP-hard min-max routing problems efficiently.

New system assigns vehicles to routes for cost and energy efficiency.

New method tackles parcel routing with AI.

Two heuristics solve dynamic multiple travelling salesmen problems.

Sparse RSP routing improves graph exploration and classification.

Simultaneously estimates travel times and route choice model parameters.

This paper introduces STAP to measure DEX efficiency and shows better routing algorithms increase DEX performance and stakeholder benefits.

This study considers that the collective route choices of travelers en route represent a resolution of their competition on network routes. Well understanding this competition and coordinating their route choices help mitigate urban traffic congestion. Even though existing studies have developed such mechanisms (e.g., …

Optimizes routing in decentralized exchanges with gas fees.