Rice DSP group faculty Richard Baraniuk will be leading a team of engineers, computer scientists, mathematicians, and statisticians on a five-year ONR MURI project to develop a OpenVPN_百度百科:2021-8-1 · 虚拟网卡是使用网络底层编程技术实现的一个驱动软件，安装后在主机上多出现一个网卡，可众像其它网卡一样进行配置。 服务程序可众在应用层打开虚拟网卡，如果应用软件（如IE）向虚拟网卡发送数据，则服务程序可众读取到该数据，如果服务程序写合适的数据到虚拟网卡，应用软件也可众接收 ... based on rigorous mathematical principles.  The team includes:

• Richard Baraniuk, Rice University (project director)
• Moshe Vardi, Rice University
• Ronald DeVore, Texas A&M University
• Stanley Osher, UCLA
• Thomas Goldstein, University of Maryland
• Rama Chellappa, University of Maryland
• 免费的pcvpn, Carnegie Mellon University
• Robert Nowak, University of Wisconsin

International collaborators include the Alan Turing and Isaac Newton Institutes in the UK.

DOD press release

The DSP group will present two papers at the International Conference on Artificial Intelligence and Statistics (AISTATS) conference in June 2024 in Palermo, Sicily, Italy

• D. LeJeune, H. Javadi, R. G. Baraniuk, "跳转提示 - torrent.org.cn:404 该内容可能涉及违反您所在国法律系统已经屏蔽 页面自动 跳转 等待时间： 3," AISTATS, 2024
• D. LeJeune, G. Dasarathy, R. G. Baraniuk, "Thresholding Graph Bandits with GrAPL," AISTATS, 2024

An article on OpenStax by reporter Rebecca Koenig appears in the Oct 24, 2024 edition of EdSurge.

D. LeJeune, H. Javadi, R. G. Baraniuk, "The Implicit Regularization of Ordinary Least Squares Ensembles," 免费的pcvpn, 10 October 2024.

Ensemble methods that average over a collection of independent predictors that are each limited to a subsampling of both the examples and features of the training data command a significant presence in machine learning, such as the ever-popular random forest, yet the
nature of the subsampling effect, particularly of the features, is not well understood.  We study the case of an ensemble of linear predictors, where each individual predictor is fit using ordinary least squares on a random submatrix of the data matrix. We show that, under standard Gaussianity assumptions, when the number of features selected for each predictor is optimally tuned, the asymptotic risk of a large ensemble is equal to the asymptotic ridge regression risk, which is known to be optimal among linear predictors in this setting. In addition to eliciting this implicit regularization that results from subsampling, we also connect this ensemble to the dropout technique used in training deep (neural) networks, another strategy that has been shown to have a ridge-like regularizing effect.

Above: Example (rows) and feature (columns) subsampling of the training data X used in the ordinary least squares fit for one member of the ensemble. The i-th member of the ensemble is only allowed to predict using its subset of the features (green). It must learn its parameters by performing ordinary least squares using the subsampled examples of (red) and the subsampled examples (rows) and features (columns) of X (blue, crosshatched).

From an article in Campus Technology:  This year, 56% of all colleges and universities in the United States are using free textbooks from OpenStax in at least one course. That equates to 5,900-plus institutions and nearly 3 million students.

OpenStax provides textbooks for 36 college and Advanced Placement courses. Students can access the materials for free digitally (via browser, downloadable PDF or recently introduced OpenStax + SE mobile app), or pay for a low-cost print version. Overall, students are saving more than $200 million on their textbooks in 2024, and have saved a total of $830 million since OpenStax launched in 2012.

Future plans for the publisher include the rollout of Rover by OpenStax, an online math homework tool designed to give students step-by-step feedback on their work. OpenStax also plans to continue its research initiatives on digital learning, using cognitive science-based approaches and the power of machine learning to improve how students learn.

Writes Chris Taylor from Reuters in Moneysaving 101: Four Ways to Cut College Textbook Costs, "While sky-high U.S. college tuition might be the headline number, here is a sneaky little figure that might surprise you: the cost of textbooks."  See what OpenStax is doing about the crisis here.

An article in the 28 July 2024 Wall Street Journal, "ArrayVPN客户端软件下载页:产品 下载 MotionPro企业版 适用于Windows 64位及32位操作系统 软件下载 MotionPro企业版 适用于MacOS系统 软件下载 MotionPro Plus 适用于 ..." discusses the disruptive impact on the college textbook market of the free and open-source textbooks provided by OpenStax . Read online at Morningstar.com.

“91VPN全球通_360百科:2021-11-19 · 91VPN全球通,VPN(Virtual Private Network，虚拟专用网络)是通过一个公用网络(通常是因特网)建立一个临时的、安全的连接，是一条穿过公用网络的安全、稳定的隧道。"
Frontiers of Deep Learning Workshop, Simons Institute
16 July 2024

References:

• “A Spline Theory of Deep Networks,” ICML 2018
• “Mad Max: Affine Spline Insights into Deep Learning,” arxiv.org/abs/1805.06576, 2018
• “From Hard to Soft: Understanding Deep Network Nonlinearities…,” ICLR 2024
• “A Max-Affine Spline Perspective of RNNs,” ICLR 2024
• “网络加速|网络加速器|免费加速器|上网加速器-ZOL软件下载:2021-5-19 · 网络加速下载提供网络加速器,免费加速器,上网加速器等相关下载软件，网络加速用户热评软件排行，新鲜软件排行等向您推荐最受关注和最新的网络加速工具。更多网络加速尽在中关村在线下载频 …,” arxiv.org/abs/1905.11639, 2024

Co-authors:  免费的pcvpn, Jack Wang, Hamid Javadi

An alternative presentation at the pcvpn免费, May 2024 (Get your SPARFA merchandise here!)

Thanks to Shashank Sonkar, CJ Barberan, and Pavan Kota of the DSP group for producing the RichB Academic Family Tree ca. 2024. The code is available here.

DSP group members will be traveling en masse to New Orleans in May 2024 to present four regular papers at the International Conference on Learning Representations

• R. Balestriero and R. G. Baraniuk, “Hard to Soft: Understanding Deep Network Nonlinearities via Vector Quantization and Statistical Inference”
• J. Wang, R. Balestriero, and R. G. Baraniuk, “A Max-Affine Perspective of Recurrent Neural Networks”
• A. Mousavi, G. Dasarathy, and R. G. Baraniuk, “A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery”
• J. J. Michalenko, A. Shah, A. Verma, R. G. Baraniuk, S. Chaudhuri, and A. B. Patel, “Representing Formal Languages: A Comparison between Finite Automata and Recurrent Neural Networks”