Soft modularization improves sample efficiency and performance in reinforcement learning.
problem Challenges in training multiple tasks jointly in reinforcement learning.
method Explicit modularization technique on policy representation, soft modularization method.
result Improves sample efficiency and performance over strong baselines in robotics manipulation tasks.
This paper improves deep forest models with soft routing and topology learning.
problem Expensive computational costs and lack of interpretability in deep neural networks.
method Soft routing in probabilistic trees and topology learning for joint optimization.
result Empowered deep forests achieve better performance with reduced model complexity.
STAR improves equivariant and invariant representation learning by routing projection heads.
problem Redundant feature learning in equivariant and invariant representation learning.
method Soft Task-Aware Routing (STAR) for projection heads specialization.
result Lower canonical correlations between invariant and equivariant embeddings.
Design rule check is a critical step in the physical design of integrated circuits to ensure manufacturability. However, it can be done only after a time-consuming detailed routing procedure, which adds drastically to the time of design iterations. With advanced technology nodes, the outcomes of global routing and deta…
Boosts share routing for multi-task learning with flexible sparse connections.
problem Designing suitable sharing mechanisms among multiple tasks in multi-task learning.
method Proposes MTNAS framework to modularize sharing into sub-networks with sparse connections and gating.
result Demonstrates consistent improvement over single-task and typical multi-task methods while maintaining efficiency.
The present work extends the randomized shortest-paths framework (RSP), interpolating between shortest-path and random-walk routing in a network, in three directions. First, it shows how to deal with equality constraints on a subset of transition probabilities and develops a generic algorithm for solving this constrain…
OTSS learns personalized decision weights from logged decisions and outputs.
problem Learning context-specific decision weights from logged decisions and outputs.
method Output-targeted soft-segmentation model that deploys personalized decision-ready weight vectors.
result OTSS achieves the lowest mean regret in benchmark settings.
In regression tasks the distribution of the data is often too complex to be fitted by a single model. In contrast, partition-based models are developed where data is divided and fitted by local models. These models partition the input space and do not leverage the input-output dependency of multimodal-distributed data,…
Study shows pre-trained models can handle long-tailed relations well, improving classifier performance.
problem Challenges in long-tailed relation classification due to class imbalance.
method Used instance-balanced sampling to pre-train models and then improved classifier performance through attentive relation routing.
result Robust classifier with attentive relation routing achieves better long-tailed classification ability.
New analysis shows how cross-entropy training shapes attention in transformers.
problem Understanding how gradient-based learning creates the required internal geometry in transformers.
method Developed a first-order analysis of cross-entropy training effects on attention scores and values in a transformer attention head.
result Introduced an advantage-based routing law and responsibility-weighted update for attention scores and values, respectively.
Capsule networks improve performance on image classification tasks with fewer parameters.
problem Improving performance of capsule networks with fewer parameters.
method Inverted dot-product attention routing, Layer Normalization, concurrent iterative routing.
result Improves performance on benchmark datasets CIFAR-10 and CIFAR-100, and performs at-par with ResNet-18.
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.
problem Vehicle routing problems with time windows and vehicle capacities.
method Joint attention to construct multiple routes concurrently.
result JAMPR outperforms existing models on different problem sizes.
A large GPS dataset reveals that a single route often covers 60% of travel observations.
problem Limited insights from small GPS datasets on route choice behavior.
method Evaluation of path generation algorithms including link penalty, link elimination, simulation, and via-node methods.
result Modified link penalty method achieves 97% coverage, significantly higher than previous studies.
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.
problem Solving detailed routing in integrated circuits while adhering to complex design rules.
method Attention-based reinforcement learning applied to track-assignment detailed routing.
result The attention router achieves over 100x acceleration compared to a genetic router without sacrificing solution quality.
Study decision boundaries using heat diffusion and probabilistic techniques.
problem Understanding the geometry of decision boundaries in machine learning.
method Using Brownian motion and probabilistic techniques to analyze decision boundaries.
result Decision boundaries exhibit persistent 'wiggly and fuzzy' regions, even under adversarial attacks.
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…
Unified routing and arbitrage with concave continuation.
problem Combining routing and arbitrage in financial markets.
method Extending AMM trade functions to negative inputs via concave continuation.
result Unified approach unifies routing and arbitrage.
A new approach integrates inventory prediction and routing optimization for better supply chain management.
problem Optimizing efficient route selection in supply chain management with uncertain inventory demand.
method Decision-focused learning approach using neural networks to directly integrate inventory prediction and routing optimization.
result Direct integration of inventory prediction and routing optimization leads to better supply chain decisions.
Sparse routing networks with co-training prevent catastrophic forgetting in continual learning.
problem Catastrophic forgetting in neural networks trained on a sequence of tasks.
method Sparse routing networks with co-training to minimize interference between dissimilar tasks.
result Sparse routing networks with co-training outperform densely connected networks on benchmarks.
Neural LNS improves vehicle routing performance.
problem Optimizing vehicle routes with constraints.
method Integrates deep learning with large neighborhood search.
result Significantly outperforms existing methods on CVRP.
Polestar optimizes public transportation routes for efficiency and user satisfaction.
problem Difficulty in finding optimal public transportation routes due to complex networks and dynamic situations.
method Developed a Public Transportation Graph (PTG) and a route search algorithm with station binding and ranking modules.
result Demonstrated superior efficiency and user satisfaction compared to existing systems.
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.
problem Capsule networks struggle with detecting changes in compositionality.
method Introduced a loss function based on routing entropy to improve compositionality.
result Capsule networks with the new loss function better detect changes in compositionality.
This study develops methods to coordinate travel routes to reduce congestion.
problem Coordination of travel routes to reduce urban traffic congestion.
method Developed mathematical approaches to quantify coordination potential and adaptive centroid-based clustering algorithm (ACCA).
result ACCA efficiently forms proper coordination groups for CB-CRM, improving efficiency with minimal performance loss.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
Study finds average 2.02 bps loss in automated market maker routing.
problem Measuring sub-optimality in automated market maker routing.
method Three reproducible optimal benchmarks: SCO, FVO, G-FVO; bisection-based algorithm for optimal routing.
result Average 2.02 bps loss per trade, \$24 million total loss.
CARROT optimizes LLM routing by choosing the cheapest and most accurate model.
problem Optimizing query routing to the most cost-effective LLM for a given task.
method CARROT uses cost and performance estimates to select the best LLM for each query.
result CARROT is minimax optimal, selecting the best LLM for any query.
Enhanced route planning with probabilistic prediction and uncertainty sets.
problem Improving route planning reliability under uncertainty.
method CQR-GAE model integrating conformal prediction and uncertainty sets.
result Significantly outperforms baseline methods in real-world traffic scenarios.
New machine learning pipeline solves dynamic vehicle routing problems efficiently.
problem Efficiently handling same day deliveries in e-commerce logistics.
method Combination of machine learning and combinatorial optimization.
result Ranked first in the EURO Meets NeurIPS Vehicle Routing Competition.
New algorithm for traffic routing in congested conditions.
problem Optimal routing in congested traffic networks.
method Congested Bandits model, UCB algorithm, iterative least squares planner.
result No-regret algorithms for congestion-aware routing.
Optimal crypto asset routing with CFMMs, including fixed costs.
problem Optimizing order execution on a network of CFMMs with fixed costs.
method Convex optimization for no fixed costs, mixed-integer convex for fixed costs, heuristics for approximate solutions.
result Approximate solutions to optimal routing and arbitrage certification problems.
DPDP combines neural heuristics with DP for vehicle routing problems.
problem Vehicle routing problems with large scale.
method Deep Policy Dynamic Programming (DPDP) that uses a neural network policy to prioritize and restrict the DP state space.
result DPDP improves upon classical DP algorithms and outperforms neural approaches for TSP, VRP, and TSPTW.
Adaptive framework predicts stock prices better during volatile periods.
problem Inability of standard prediction models to handle regime-dependent stock market behavior.
method Autoencoder-Gated Dual Node Transformers with Reinforcement Learning Control.
result 0.59% MAPE with adaptive system, compared to 0.80% for baseline.
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false pos…
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
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.
problem Min-max routing problems with multiple agents and large-scale applications.
method Sequential planning approach with Transformer and equitable workload distribution inductive biases.
result Significant runtime and cost reductions in min-max mTSP and min-max mPDP tasks.
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…
New system assigns vehicles to routes for cost and energy efficiency.
problem Optimizing vehicle route assignment for cost and energy efficiency.
method Machine learning-based neural network algorithm to estimate energy consumption and provide real-time recommendations.
result Demonstrated efficient vehicle assignment for medium and heavy duty trucks.
Soft labeling impacts OOD detection in neural networks.
problem Impact of soft labeling on OOD detection in deep neural networks.
method Empirical analysis of how soft labeling affects OOD detection performance.
result Soft labeling can either improve or deteriorate OOD detection performance.
New method tackles parcel routing with AI.
problem Routing parcels efficiently through a network of hubs.
method Combines graph neural networks with model-free RL.
result Extracts small feature graphs from the environment state.
Developed a new thresholding method that connects soft and hard thresholding.
problem Connecting soft and hard thresholding methods in data analysis.
method Scaled soft thresholding method with empirical scaling values.
result Found two sources of over-fitting in the scaled soft thresholding method.
Two heuristics solve dynamic multiple travelling salesmen problems.
problem Dynamic routing with unknown customers.
method Balanced dynamic closest vehicle heuristic and balanced dynamic assignment vehicle heuristic.
result Continuous approximation models for strategic dynamic routing.