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.
Client-based machine learning uses mobile devices for computation, improving privacy and reducing data upload.
This paper proposes a cooperative mechanism for mitigating the performance degradation due to non-independent-and-identically-distributed (non-IID) data in collaborative machine learning (ML), namely federated learning (FL), which trains an ML model using the rich data and computational resources of mobile clients with…
OpenML is an online machine learning platform where researchers can easily share data, machine learning tasks and experiments as well as organize them online to work and collaborate more efficiently. In this paper, we present an R package to interface with the OpenML platform and illustrate its usage in combination wit…
Mix2FLD improves FL accuracy with FD, reducing convergence time.
Paper proposes a method to reuse models without raw data.
FedDST trains sparse sub-networks to improve efficiency in federated learning.
A new framework reduces data upload for image classification while protecting user privacy.
In order to improve mobile data transparency, a number of network-based approaches have been proposed to inspect packets generated by mobile devices and detect personally identifiable information (PII), ad requests, or other activities. State-of-the-art approaches train classifiers based on features extracted from HTTP…
Researchers develop a framework for quantum machine learning models.
Although we have tons of machine learning tools to analyze data, most of them require users have some programming backgrounds. Here we introduce a SaaS application which allows users analyze their data without any coding and even without any knowledge of machine learning. Users can upload, train, predict and download t…
FedPAQ improves federated learning efficiency by averaging and quantizing updates.
Secure submodel learning protects privacy in federated learning.
Deep Learning techniques have achieved remarkable results in many domains. Often, training deep learning models requires large datasets, which may require sensitive information to be uploaded to the cloud to accelerate training. To adequately protect sensitive information, we propose distributed layer-partitioned train…
We propose a cloud-based filter trained to block third parties from uploading privacy-sensitive images of others to online social media. The proposed filter uses Distributed One-Class Learning, which decomposes the cloud-based filter into multiple one-class classifiers. Each one-class classifier captures the properties…
This project report compares some known GAN and VAE models proposed prior to 2017. There has been significant progress after we finished this report. We upload this report as an introduction to generative models and provide some personal interpretations supported by empirical evidence. Both generative adversarial netwo…
LC-FL uses generative models to reduce communication costs in federated learning.
Active Federated Learning selects clients to maximize efficiency.
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication al…
In this work, the time chart of Dow Jones Industrial Average (DJIA) index is analyzed and approach of recession time term is predicted, which may be hallmark of a worldwide economic crisis. However, the methods used for the prediction will be disclosed a few years from now. On the other hand, this work will be updated …
FMP sampling improves model calibration without sharing data.
OpenML-Python API simplifies access to OpenML for Python users.
Keyword spotting--or wakeword detection--is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotti…
Planetary exploration missions with Mars rovers are complicated, which generally require elaborated task planning by human experts, from the path to take to the images to capture. NASA has been using this process to acquire over 22 million images from the planet Mars. In order to improve the degree of automation and th…
Robust high-dimensional data processing has witnessed an exciting development in recent years, as theoretical results have shown that it is possible using convex programming to optimize data fit to a low-rank component plus a sparse outlier component. This problem is also known as Robust PCA, and it has found applicati…
Deeper quantum circuits can improve performance on unseen data, contrary to traditional views.
Photonic quantum reinforcement learning for control problems.
We prove every oriented compact cyclic -orbifold has a contact structure. There is another proof in the web by Daniel Herr in his uploaded thesis which depends on open book decompositions, ours is independent of that. We define overtwisted contact structures, tight contact structures and Lutz twist on oriented compa…
Federated learning obtains a central model on the server by aggregating models trained locally on clients. As a result, federated learning does not require clients to upload their data to the server, thereby preserving the data privacy of the clients. One challenge in federated learning is to reduce the client-server c…
Quantum neural networks need both data-dependent and trainable unitaries for effective geometric deformation.
The social media revolution has produced a plethora of web services to which users can easily upload and share multimedia documents. Despite the popularity and convenience of such services, the sharing of such inherently personal data, including speech data, raises obvious security and privacy concerns. In particular, …
Mobile app improves speech recognition of names with user feedback.
Paper proposes DP-PASGD for efficient, private IoT learning.
Paper proposes a method to compress deep learning models using PU setting and cloud data.
Scheduling and power allocation improve federated learning efficiency in NOMA networks.
Paper develops a federated learning method to protect privacy without sacrificing model utility.
Glyph speeds up DNN training on encrypted data by 99%.
Federated CTMC model estimates bridge deterioration hazards without sharing raw data.
Yelp has been one of the most popular local service search engine in US since 2004. It is powered by crowd-sourced text reviews and photo reviews. Restaurant customers and business owners upload photo images to Yelp, including reviewing or advertising either food, drinks, or inside and outside decorations. It is obviou…
This is an expository article on the theory of Kuranishi structure and is based on a series of pdf files we uploaded for the discussion of the google group named `Kuranishi' (with its administrator H. Hofer). There we replied to several questions concerning Kuranishi structure raised by K. Wehrheim. At this stage we su…
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue, we introduce two novel strategies to reduce communication costs: (1) the use of lossy compression on the global model sent server-to-client;…
FTTQ optimizes quantized networks in federated learning, reducing communication costs.
SCBF preserves medical data privacy by training models without sharing inputs.
CHEETAH speeds up secure MLaaS by 100x over fastest existing schemes.
A crowdsourcing framework improves communication efficiency in federated learning.
LASG improves communication efficiency in distributed learning.
Study quantifies impacts of heterogeneity in FL on smartphone data.
Personalized deep learning reduces inappropriate shocks in VA detection.
The paper shows how curated synthetic data can optimize human preferences in generative models.