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Found 14 papers, 1 papers with code
Wakeword Detection under Distribution Shifts
To train effectively with a mix of human and teacher labeled data, we develop a teacher labeling strategy based on confidence heuristics to reduce entropy on the label distribution from the teacher model; the data is then sampled to match the marginal distribution on the labels.
Latency Control for Keyword Spotting
We also show that when our approach is used in conjunction with a max-pooling loss, we are able to improve relative false accepts by 25 % at a fixed latency when compared to cross entropy loss.
Self-Healing Small-Scale Swimmers
Herein, self-healing small-scale swimmers capable of autonomous propulsion and on-the-fly structural recovery are described.
Soft Condensed Matter Materials Science
Probabilistic Semantic Retrieval for Surveillance Videos with Activity Graphs
We present a novel framework for finding complex activities matching user-described queries in cluttered surveillance videos.
Field of Groves: An Energy-Efficient Random Forest
In this work, we present a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to CNNs and SVMs under tight energy budgets.
Adaptive Neural Networks for Efficient Inference
We first pose an adaptive network evaluation scheme, where we learn a system to adaptively choose the components of a deep network to be evaluated for each example.
Pruning Random Forests for Prediction on a Budget
We propose to prune a random forest (RF) for resource-constrained prediction.
Resource Constrained Structured Prediction
We study the problem of structured prediction under test-time budget constraints.
Optimally Pruning Decision Tree Ensembles With Feature Cost
We propose a novel 0-1 integer program formulation for ensemble pruning.
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction
We learn node policies in the DAG by reducing the global objective to a series of cost sensitive learning problems.
Sensor Selection by Linear Programming
We decompose the problem, which is known to be intractable, into combinatorial (tree structures) and continuous parts (node decision rules) and propose to solve them separately.
Feature-Budgeted Random Forest
We seek decision rules for prediction-time cost reduction, where complete data is available for training, but during prediction-time, each feature can only be acquired for an additional cost.
Max-Cost Discrete Function Evaluation Problem under a Budget
We develop a broad class of \emph{admissible} impurity functions that admit monomials, classes of polynomials, and hinge-loss functions that allow for flexible impurity design with provably optimal approximation bounds.
Local Supervised Learning through Space Partitioning
We show that space partitioning can be equivalently reformulated as a supervised learning problem and consequently any discriminative learning method can be utilized in conjunction with our approach.
