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Found 27 papers, 7 papers with code
Inverse classification with logistic and softmax classifiers: efficient optimization
Specifically, one wants to find the closest instance to a given input instance such that the classifier's predicted label is changed in a desired way.
Solving Recurrence Relations using Machine Learning, with Application to Cost Analysis
Automatic static cost analysis infers information about the resources used by programs without actually running them with concrete data, and presents such information as functions of input data sizes.
Very fast, approximate counterfactual explanations for decision forests
We consider finding a counterfactual explanation for a classification or regression forest, such as a random forest.
Towards Better Decision Forests: Forest Alternating Optimization
However, unlike for most other models, such as neural networks, optimizing forests or trees is not easy, because they define a non-differentiable function.
Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees
Ensemble methods based on decision trees, such as Random Forests or boosted forests, have long been established as some of the most powerful, off-the-shelf machine learning models, and have been widely used in computer vision and other areas.
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions
However, VGG nets can be better compressed by combining low-rank with a few floating point weights.
Sparse Oblique Decision Trees: A Tool to Understand and Manipulate Neural Net Features
The widespread deployment of deep nets in practical applications has lead to a growing desire to understand how and why such black-box methods perform prediction.
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms
We consider counterfactual explanations, the problem of minimally adjusting features in a source input instance so that it is classified as a target class under a given classifier.
A flexible, extensible software framework for model compression based on the LC algorithm
We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.
An Experimental Comparison of Old and New Decision Tree Algorithms
This paper presents a detailed comparison of a recently proposed algorithm for optimizing decision trees, tree alternating optimization (TAO), with other popular, established algorithms.
Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets
This inverse set is a complicated high dimensional object that we explore by an optimization-based sampling approach.
Style Transfer by Rigid Alignment in Neural Net Feature Space
Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image.
Model compression as constrained optimization, with application to neural nets. Part II: quantization
We consider the problem of deep neural net compression by quantization: given a large, reference net, we want to quantize its real-valued weights using a codebook with $K$ entries so that the training loss of the quantized net is minimal.
Model compression as constrained optimization, with application to neural nets. Part I: general framework
Then, we give a general algorithm to optimize this nonconvex problem based on the augmented Lagrangian and alternating optimization.
ParMAC: distributed optimisation of nested functions, with application to learning binary autoencoders
A general, recent approach to optimise such "nested" functions is the method of auxiliary coordinates (MAC).
An ensemble diversity approach to supervised binary hashing
They ensure that the hash functions differ from each other through constraints or penalty terms that encourage codes to be orthogonal or dissimilar across bits, but this couples the binary variables and complicates the already difficult optimization.
A review of mean-shift algorithms for clustering
A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data.
Optimizing affinity-based binary hashing using auxiliary coordinates
Recent work has tried to optimize the objective directly over the binary codes and achieved better results, but the hash function was still learned a posteriori, which remains suboptimal.
Hashing with binary autoencoders
An attractive approach for fast search in image databases is binary hashing, where each high-dimensional, real-valued image is mapped onto a low-dimensional, binary vector and the search is done in this binary space.
An ADMM algorithm for solving a proximal bound-constrained quadratic program
We consider a proximal operator given by a quadratic function subject to bound constraints and give an optimization algorithm using the alternating direction method of multipliers (ADMM).
The Laplacian K-modes algorithm for clustering
In addition to finding meaningful clusters, centroid-based clustering algorithms such as K-means or mean-shift should ideally find centroids that are valid patterns in the input space, representative of data in their cluster.
The role of dimensionality reduction in linear classification
Using the method of auxiliary coordinates, we give a simple, efficient algorithm to train a combination of nonlinear DR and a classifier, and apply it to a RBF mapping with a linear SVM.
LASS: a simple assignment model with Laplacian smoothing
We consider the problem of learning soft assignments of $N$ items to $K$ categories given two sources of information: an item-category similarity matrix, which encourages items to be assigned to categories they are similar to (and to not be assigned to categories they are dissimilar to), and an item-item similarity matrix, which encourages similar items to have similar assignments.
Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application
We provide an elementary proof of a simple, efficient algorithm for computing the Euclidean projection of a point onto the probability simplex.
The K-modes algorithm for clustering
Many clustering algorithms exist that estimate a cluster centroid, such as K-means, K-medoids or mean-shift, but no algorithm seems to exist that clusters data by returning exactly K meaningful modes.
A Denoising View of Matrix Completion
In matrix completion, we are given a matrix where the values of only some of the entries are present, and we want to reconstruct the missing ones.
People Tracking with the Laplacian Eigenmaps Latent Variable Model
Reliably recovering 3D human pose from monocular video requires constraints that bias the estimates towards typical human poses and motions.
