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Found 14 papers, 6 papers with code
OrcoDCS: An IoT-Edge Orchestrated Online Deep Compressed Sensing Framework
However, the existing compressed data aggregation (CDA) frameworks (e. g., compressed sensing-based data aggregation, deep learning(DL)-based data aggregation) do not possess the flexibility and adaptivity required to handle distinct sensing tasks and environmental changes.
Parity Calibration
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather than the actual value of the future observation.
Online Platt Scaling with Calibeating
We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression.
Faster online calibration without randomization: interval forecasts and the power of two choices
We study the problem of making calibrated probabilistic forecasts for a binary sequence generated by an adversarial nature.
Top-label calibration and multiclass-to-binary reductions
We propose top-label calibration as a rectification of confidence calibration.
Distribution-free calibration guarantees for histogram binning without sample splitting
We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001].
Modern Machine and Deep Learning Systems as a way to achieve Man-Computer Symbiosis
Man-Computer Symbiosis (MCS) was originally envisioned by the famous computer pioneer J. C. R.
Distribution-free binary classification: prediction sets, confidence intervals and calibration
We study three notions of uncertainty quantification -- calibration, confidence intervals and prediction sets -- for binary classification in the distribution-free setting, that is without making any distributional assumptions on the data.
Shallow Encoder Deep Decoder (SEDD) Networks for Image Encryption and Decryption
This paper explores a new framework for lossy image encryption and decryption using a simple shallow encoder neural network E for encryption, and a complex deep decoder neural network D for decryption.
Self learning robot using real-time neural networks
With the advancements in high volume, low precision computational technology and applied research on cognitive artificially intelligent heuristic systems, machine learning solutions through neural networks with real-time learning has seen an immense interest in the research community as well the industry.
Nested conformal prediction and quantile out-of-bag ensemble methods
Conformal prediction is a popular tool for providing valid prediction sets for classification and regression problems, without relying on any distributional assumptions on the data.
Path Length Bounds for Gradient Descent and Flow
We derive bounds on the path length $\zeta$ of gradient descent (GD) and gradient flow (GF) curves for various classes of smooth convex and nonconvex functions.
Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
This paper studies the problem of sparse regression where the goal is to learn a sparse vector that best optimizes a given objective function.
ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices
Such applications demand prediction models with small storage and computational complexity that do not compromise significantly on accuracy.
