Share This Author
Travel time estimation of a path using sparse trajectories
In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS…
Same-decision probability: A confidence measure for threshold-based decisions
Language Generation via Combinatorial Constraint Satisfaction: A Tree Search Enhanced Monte-Carlo Approach
- Maosen Zhang, Nan Jiang, Lei Li, Yexiang Xue
- Computer ScienceFindings of the Association for Computational…
- 1 November 2020
This work proposes TSMC, an efficient method to generate high likelihood sentences with respect to a pre-trained language model while satisfying the constraints, which is highly flexible, requires no task-specific train- ing, and leverages efficient constraint satisfaction solving techniques.
Reducing greenhouse gas emissions of Amazon hydropower with strategic dam planning
The authors estimate the range of GHG emission intensities expected for 351 proposed and 158 existing Amazon dams and find that existing Amazon hydropower reservoirs collectively emit 14 Tg CO2eq per year, and that if all proposed Amazon dams are built, annual emissions would increase 5-fold.
Expanding Holographic Embeddings for Knowledge Completion
The number of perturbed copies needed to provably recover the full entity-entity or entity-relation interaction matrix is formally characterized, leveraging ideas from Haar wavelets and compressed sensing.
Automated Phase Mapping with AgileFD and its Application to Light Absorber Discovery in the V-Mn-Nb Oxide System.
AgileFD, an artificial intelligence algorithm that enables rapid phase mapping from a combinatorial library of X-ray diffraction patterns, is developed and demonstrated through investigation of the V-Mn-Nb oxide system where decomposition of eight oxide phases provides the first phase map for this pseudoternary system.
Basing Decisions on Sentences in Decision Diagrams
This paper identifies a class of Boolean functions where basing decisions on sentences using dissections of a variable order can lead to exponentially more compact SDDs, compared to OBDDs based on the same variable order.
End-to-End Learning for the Deep Multivariate Probit Model
This work proposes a flexible deep generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP), which is an end-to-end learning scheme that uses an efficient parallel sampling process of the multivariate probit model to exploit GPU-boosted deep neural networks.
Learning Large-Scale Dynamic Discrete Choice Models of Spatio-Temporal Preferences with Application to Migratory Pastoralism in East Africa
The problem of inferring agents' preferences from observed movement trajectories is considered as an Inverse Reinforcement Learning (IRL) problem and a probabilistic approach is taken and generative models that can be used to simulate behavior under new circumstances such as changes in resource availability, access policies, or climate are considered.
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
XOR_MMAP provides a constant factor approximation to the Marginal MAP problem, by encoding it as a single optimization in a polynomial size of the original problem, which represents the intractable counting subproblem with queries to NP oracles, subject to additional parity constraints.