A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New method uses VAEs for blind channel equalization and decoding.
New DL algorithm estimates OFDM channels without pilots.
New method uses single quantum state for machine learning tasks, improving accuracy.
Blind single-channel source separation is a long standing signal processing challenge. Many methods were proposed to solve this task utilizing multiple signal priors such as low rank, sparsity, temporal continuity etc. The recent advance of generative adversarial models presented new opportunities in signal regression …
Optimal joint separation condition for radar and communications channels in dual-blind deconvolution.
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely red…
New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmit…
Paper solves complex signal processing problem efficiently.
New method recovers radar and communication signals from overlaid data.
The study reveals flaws in pruning criteria and proposes a new assumption for better filter selection.
In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channe…
FPGA-based multi-layer equalizer adapts to changing channels.
The paper proposes a method to identify power system oscillation modes using blind source separation.
A new method detects hidden driving forces in systems with multiple observables.
Over-the-air computation (AirComp) shows great promise to support fast data fusion in Internet-of-Things (IoT) networks. AirComp typically computes desired functions of distributed sensing data by exploiting superposed data transmission in multiple access channels. To overcome its reliance on channel station informatio…
New method recovers kernel and sparse inputs from convolved data efficiently.
This work examines a semi-blind single-channel source separation problem. Our specific aim is to separate one source whose local structure is approximately known, from another a priori unspecified background source, given only a single linear combination of the two sources. We propose a separation technique based on lo…
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on de…
Paper proposes a time-frequency analysis method for blind modulation classification in MIMO systems.
Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more …
Edge devices learn a global model collaboratively over wireless channels.
In this paper, we investigate a new compressive sensing model for multi-channel sparse data where each channel can be represented as a hierarchical tree and different channels are highly correlated. Therefore, the full data could follow the forest structure and we call this property as \emph{forest sparsity}. It exploi…
Bayesian Monte-Carlo method assesses uncertainty in shear stress entropy models.
Blind Descent avoids gradient issues, using a different learning approach.
The paper reconciles two conflicting fairness criteria in algorithmic risk scores.
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…
New divergences improve score-based methods for multi-modal distributions.
The article derives some novel independence measures and contrast functions for Blind Source Separation (BSS) application. For the order differentiable multivariate functions with equal hyper-volumes (region bounded by hyper-surfaces) and with a constraint of bounded support for , it proves that equality …
New method improves accuracy of quantized neural networks.
New method solves blind inverse problems by optimizing both operator and image parameters.
Proposes HOTA-FedGradNorm for faster and robust PFL in noisy channels.
In many statistical problems, a more coarse-grained model may be suitable for population-level behaviour, whereas a more detailed model is appropriate for accurate modelling of individual behaviour. This raises the question of how to integrate both types of models. Methods such as posterior regularization follow the id…
The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), …
We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find t…
A new method for blind source separation using hierarchical structure and KL divergence.
Spatial blind source separation simplifies multivariate spatial prediction.
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high dimensional space. Unlike …
This paper bounds min-entropy leakage for Blowfish privacy using graph symmetries.
Adversarial perturbations are more effective in Y-channel of YCbCr color space.
New insights into learning for blind inverse problems with theoretical guarantees.
Origami uses SGX enclaves and blinding to protect deep neural network inference privacy.
The paper tackles fair classification with multiple sensitive features.
We revisit the Blind Deconvolution problem with a focus on understanding its robustness and convergence properties. Provable robustness to noise and other perturbations is receiving recent interest in vision, from obtaining immunity to adversarial attacks to assessing and describing failure modes of algorithms in missi…
Several methods of estimating the mutual information of random variables have been developed in recent years. They can prove valuable for novel approaches to learning statistically independent features. In this paper, we use one of these methods, a mutual information neural estimation (MINE) network, to present a proof…
Paper tackles blind polynomial regression for unknown inputs.
A framework to quantify deployment risk in ML systems, especially for rare states.
Agents trained in simulation may make errors in the real world due to mismatches between training and execution environments. These mistakes can be dangerous and difficult to discover because the agent cannot predict them a priori. We propose using oracle feedback to learn a predictive model of these blind spots to red…
This paper considers probabilistic estimation of a low-rank matrix from non-linear element-wise measurements of its elements. We derive the corresponding approximate message passing (AMP) algorithm and its state evolution. Relying on non-rigorous but standard assumptions motivated by statistical physics, we characteriz…