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Found 15 papers, 1 papers with code
Estimating Joint Probability Distribution With Low-Rank Tensor Decomposition, Radon Transforms and Dictionaries
In this paper, we describe a method for estimating the joint probability density from data samples by assuming that the underlying distribution can be decomposed as a mixture of product densities with few mixture components.
Cooperative Multi-Agent Reinforcement Learning for Inventory Management
In this work, we present a system with a custom GPU-parallelized environment that consists of one warehouse and multiple stores, a novel architecture for agent-environment dynamics incorporating enhanced state and action spaces, and a shared reward specification that seeks to optimize for a large retailer's supply chain needs.
Analysis of Tomographic Reconstruction of 2D Images using the Distribution of Unknown Projection Angles
More recently, it has been proved that one can reconstruct a 1D band-limited signal even if the exact sample locations are unknown, but given just the distribution of the sample locations and their ordering in 1D.
Joint Probability Estimation Using Tensor Decomposition and Dictionaries
We create a dictionary of various families of distributions by inspecting the data, and use it to approximate each decomposed factor of the product in the mixture.
A decision-tree framework to select optimal box-sizes for product shipments
In this paper, we propose a solution for the single-count shipment containing one product per box in two steps: (i) reduce it to a clustering problem in the $3$ dimensional space of length, width and height where each cluster corresponds to the group of products that will be shipped in a particular size variant, and (ii) present an efficient forward-backward decision tree based clustering method with low computational complexity on $N$ and $K$ to obtain these $K$ clusters and corresponding box dimensions.
Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages
We develop a Causal-Deep Neural Network (CDNN) model trained in two stages to infer causal impact estimates at an individual unit level.
Recovery of Joint Probability Distribution from one-way marginals: Low rank Tensors and Random Projections
Joint probability mass function (PMF) estimation is a fundamental machine learning problem.
SPOT: A framework for selection of prototypes using optimal transport
In this work, we develop an optimal transport (OT) based framework to select informative prototypical examples that best represent a given target dataset.
Think out of the package: Recommending package types for e-commerce shipments
Multiple product attributes like dimensions, weight, fragility, liquid content etc.
Streaming Methods for Restricted Strongly Convex Functions with Applications to Prototype Selection
In this paper, we show that if the optimization function is restricted-strongly-convex (RSC) and restricted-smooth (RSM) -- a rich subclass of weakly submodular functions -- then a streaming algorithm with constant factor approximation guarantee is possible.
Efficient Data Representation by Selecting Prototypes with Importance Weights
Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract.
Sensitivity Analysis for additive STDP rule
Spike Timing Dependent Plasticity (STDP) is a Hebbian like synaptic learning rule.
A fast eikonal equation solver using the Schrodinger wave equation
In our framework, a solution to the eikonal equation is obtained in the limit as Planck's constant $\hbar$ (treated as a free parameter) tends to zero of the solution to the corresponding linear Schr\"odinger equation.
Gradient density estimation in arbitrary finite dimensions using the method of stationary phase
We prove that the density function of the gradient of a sufficiently smooth function $S : \Omega \subset \mathbb{R}^d \rightarrow \mathbb{R}$, obtained via a random variable transformation of a uniformly distributed random variable, is increasingly closely approximated by the normalized power spectrum of $\phi=\exp\left(\frac{iS}{\tau}\right)$ as the free parameter $\tau \rightarrow 0$.
A new variational principle for the Euclidean distance function: Linear approach to the non-linear eikonal problem
In other words, when $S$ and $\phi$ are related by $\phi = \exp \left(-\frac{S}{\tau} \right)$ and $\phi$ satisfies a specific linear differential equation corresponding to the extremum of a variational problem, we obtain the approximate Euclidean distance function $S = -\tau \log(\phi)$ which converges to the true solution in the limit as $\tau \rightarrow 0$.
