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Found 42 papers, 23 papers with code
Vector Quantile Regression on Manifolds
Quantile regression (QR) is a statistical tool for distribution-free estimation of conditional quantiles of a target variable given explanatory features.
Designing Nonlinear Photonic Crystals for High-Dimensional Quantum State Engineering
We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics.
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling
The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot.
Transformer Vs. MLP-Mixer: Exponential Expressive Gap For NLP Problems
Vision-Transformers are widely used in various vision tasks.
Random Search Hyper-Parameter Tuning: Expected Improvement Estimation and the Corresponding Lower Bound
However, the expected accuracy improvement from every additional search iteration, is still unknown.
Physical Passive Patch Adversarial Attacks on Visual Odometry Systems
While such perturbations are usually discussed as tailored to a specific input, a universal perturbation can be constructed to alter the model's output on a set of inputs.
Fast Nonlinear Vector Quantile Regression
Despite its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target $\boldsymbol{\mathrm{Y}}$ given the features $\boldsymbol{\mathrm{X}}$; (ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles; (iii) no fast or scalable solvers for VQR currently exist.
Towards Predicting Fine Finger Motions from Ultrasound Images via Kinematic Representation
A central challenge in building robotic prostheses is the creation of a sensor-based system able to read physiological signals from the lower limb and instruct a robotic hand to perform various tasks.
SPDCinv: Inverse Quantum-Optical Design of High-Dimensional Qudits
In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels.
Ranked #1 on
Image Classification
on CIFAR-10 (with noisy labels)
Inverse Design of Quantum Holograms in Three-Dimensional Nonlinear Photonic Crystals
We introduce a systematic approach for designing 3D nonlinear photonic crystals and pump beams for generating desired quantum correlations between structured photon-pairs.
SimuGAN: Unsupervised forward modeling and optimal design of a LIDAR Camera
Energy-saving LIDAR camera for short distances estimates an object's distance using temporally intensity-coded laser light pulses and calculates the maximum correlation with the back-scattered pulse.
Digital Gimbal: End-to-end Deep Image Stabilization with Learnable Exposure Times
Mechanical image stabilization using actuated gimbals enables capturing long-exposure shots without suffering from blur due to camera motion.
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
Unsupervised learning has always been appealing to machine learning researchers and practitioners, allowing them to avoid an expensive and complicated process of labeling the data.
Ranked #1 on
Unsupervised Image Classification
on ObjectNet
Data-Driven Prediction of Embryo Implantation Probability Using IVF Time-lapse Imaging
The process of fertilizing a human egg outside the body in order to help those suffering from infertility to conceive is known as in vitro fertilization (IVF).
HCM: Hardware-Aware Complexity Metric for Neural Network Architectures
Convolutional Neural Networks (CNNs) have become common in many fields including computer vision, speech recognition, and natural language processing.
Colored Noise Injection for Training Adversarially Robust Neural Networks
Even though deep learning has shown unmatched performance on various tasks, neural networks have been shown to be vulnerable to small adversarial perturbations of the input that lead to significant performance degradation.
Smoothed Inference for Adversarially-Trained Models
In this work, we study the application of randomized smoothing as a way to improve performance on unperturbed data as well as to increase robustness to adversarial attacks.
Loss Aware Post-training Quantization
We show that with more aggressive quantization, the loss landscape becomes highly non-separable with steep curvature, making the selection of quantization parameters more challenging.
CAT: Compression-Aware Training for bandwidth reduction
Our method trains the model to achieve low-entropy feature maps, which enables efficient compression at inference time using classical transform coding methods.
Baby steps towards few-shot learning with multiple semantics
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems.
Ranked #9 on
Few-Shot Image Classification
on Mini-ImageNet - 1-Shot Learning
(using extra training data)
Feature Map Transform Coding for Energy-Efficient CNN Inference
We analyze the performance of our approach on a variety of CNN architectures and demonstrate that FPGA implementation of ResNet-18 with our approach results in a reduction of around 40% in the memory energy footprint, compared to quantized network, with negligible impact on accuracy.
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
While mainstream deep learning methods train the neural networks weights while keeping the network architecture fixed, the emerging neural architecture search (NAS) techniques make the latter also amenable to training.
LaSO: Label-Set Operations networks for multi-label few-shot learning
We conduct numerous experiments showing promising results for the label-set manipulation capabilities of the proposed approach, both directly (using the classification and retrieval metrics), and in the context of performing data augmentation for multi-label few-shot learning.
Beholder-GAN: Generation and Beautification of Facial Images with Conditioning on Their Beauty Level
Beauty is in the eye of the beholder.
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
While most works use uniform quantizers for both parameters and activations, it is not always the optimal one, and a non-uniform quantizer need to be considered.
High quality ultrasonic multi-line transmission through deep learning
Frame rate is a crucial consideration in cardiac ultrasound imaging and 3D sonography.
High frame-rate cardiac ultrasound imaging with deep learning
The network achieves a significant improvement in image quality for both $5-$ and $7-$line MLA resulting in a decorrelation measure similar to that of SLA while having the frame rate of MLA.
Class-Aware Fully-Convolutional Gaussian and Poisson Denoising
We propose a fully-convolutional neural-network architecture for image denoising which is simple yet powerful.
RepMet: Representative-based metric learning for classification and one-shot object detection
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples.
Delta-encoder: an effective sample synthesis method for few-shot object recognition
Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it.
UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks
We present a novel method for neural network quantization that emulates a non-uniform $k$-quantile quantizer, which adapts to the distribution of the quantized parameters.
DeepISP: Towards Learning an End-to-End Image Processing Pipeline
We present DeepISP, a full end-to-end deep neural model of the camera image signal processing (ISP) pipeline.
Towards CT-quality Ultrasound Imaging using Deep Learning
The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis.
Subspace Least Squares Multidimensional Scaling
Multidimensional Scaling (MDS) is one of the most popular methods for dimensionality reduction and visualization of high dimensional data.
Computational Geometry
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
We introduce a new framework for learning dense correspondence between deformable 3D shapes.
Deep Convolutional Denoising of Low-Light Images
Poisson distribution is used for modeling noise in photon-limited imaging.
Deep Class Aware Denoising
We further show that a significant boost in performance of up to $0. 4$ dB PSNR can be achieved by making our network class-aware, namely, by fine-tuning it for images belonging to a specific semantic class.
Tradeoffs between Convergence Speed and Reconstruction Accuracy in Inverse Problems
Solving inverse problems with iterative algorithms is popular, especially for large data.
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?
Three important properties of a classification machinery are: (i) the system preserves the core information of the input data; (ii) the training examples convey information about unseen data; and (iii) the system is able to treat differently points from different classes.
On the Stability of Deep Networks
In particular, we formally prove in the longer version that DNN with random Gaussian weights perform a distance-preserving embedding of the data, with a special treatment for in-class and out-of-class data.
Sparse similarity-preserving hashing
In recent years, a lot of attention has been devoted to efficient nearest neighbor search by means of similarity-preserving hashing.
