| 3 | -- | 0 | Andrea J. Goldsmith. Welcome to the IEEE Journal on Selected Areas in Information Theory (JSAIT) |
| 4 | -- | 0 | Richard G. Baraniuk, Alex Dimakis, Negar Kiyavash, Sewoong Oh, Rebecca Willett. Guest Editorial |
| 5 | -- | 18 | Hyeji Kim, Sewoong Oh, Pramod Viswanath. Physical Layer Communication via Deep Learning |
| 19 | -- | 38 | Ziv Goldfeld, Yury Polyanskiy. The Information Bottleneck Problem and its Applications in Machine Learning |
| 39 | -- | 56 | Gregory Ongie, Ajil Jalal, Christopher A. Metzler, Richard G. Baraniuk, Alexandros G. Dimakis, Rebecca Willett. Deep Learning Techniques for Inverse Problems in Imaging |
| 57 | -- | 66 | Nadav Dym, Barak Sober, Ingrid Daubechies. Expression of Fractals Through Neural Network Functions |
| 67 | -- | 83 | Vidya Muthukumar, Kailas Vodrahalli, Vignesh Subramanian, Anant Sahai. Harmless Interpolation of Noisy Data in Regression |
| 84 | -- | 105 | Samet Oymak, Mahdi Soltanolkotabi. Toward Moderate Overparameterization: Global Convergence Guarantees for Training Shallow Neural Networks |
| 106 | -- | 120 | Christopher Snyder, Sriram Vishwanath. Sample Compression, Support Vectors, and Generalization in Deep Learning |
| 121 | -- | 130 | Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli. Tightening Mutual Information-Based Bounds on Generalization Error |
| 131 | -- | 144 | Ankit Pensia, Varun Jog, Po-Ling Loh. Extracting Robust and Accurate Features via a Robust Information Bottleneck |
| 145 | -- | 156 | Farzan Farnia, Jesse M. Zhang, David N. C. Tse. A Fourier-Based Approach to Generalization and Optimization in Deep Learning |
| 157 | -- | 166 | Shao-Lun Huang, Xiangxiang Xu 0001, Lizhong Zheng. An Information-Theoretic Approach to Unsupervised Feature Selection for High-Dimensional Data |
| 167 | -- | 177 | Mina Karzand, Robert D. Nowak. MaxiMin Active Learning in Overparameterized Model Classes |
| 178 | -- | 193 | David Burth Kurka, Deniz Gündüz. DeepJSCC-f: Deep Joint Source-Channel Coding of Images With Feedback |
| 194 | -- | 206 | Hyeji Kim, Yihan Jiang, Sreeram Kannan, Sewoong Oh, Pramod Viswanath. Deepcode: Feedback Codes via Deep Learning |
| 207 | -- | 216 | Yihan Jiang, Hyeji Kim, Himanshu Asnani, Sreeram Kannan, Sewoong Oh, Pramod Viswanath. LEARN Codes: Inventing Low-Latency Codes via Recurrent Neural Networks |
| 217 | -- | 226 | Debraj Basu 0001, Deepesh Data, Can Karakus, Suhas N. Diggavi. Qsparse-Local-SGD: Distributed SGD With Quantization, Sparsification, and Local Computations |
| 227 | -- | 236 | Jack Kosaian, K. V. Rashmi, Shivaram Venkataraman. Learning-Based Coded Computation |
| 237 | -- | 249 | Osama A. Hanna, Yahya H. Ezzeldin, Tara Sadjadpour, Christina Fragouli, Suhas N. Diggavi. On Distributed Quantization for Classification |
| 250 | -- | 266 | Avhishek Chatterjee, Lav R. Varshney. Energy-Reliability Limits in Nanoscale Feedforward Neural Networks and Formulas |
| 267 | -- | 276 | Kunping Huang, Paul H. Siegel, Anxiao Jiang. Functional Error Correction for Robust Neural Networks |
| 277 | -- | 291 | Rawad Bitar, Mary Wootters, Salim El Rouayheb. Stochastic Gradient Coding for Straggler Mitigation in Distributed Learning |
| 292 | -- | 303 | Zhaoqiang Liu, Jonathan Scarlett. Information-Theoretic Lower Bounds for Compressive Sensing With Generative Models |
| 304 | -- | 311 | Soheil Feizi, Farzan Farnia, Tony Ginart, David Tse. Understanding GANs in the LQG Setting: Formulation, Generalization and Stability |
| 312 | -- | 323 | Pei Peng, Shirin Jalali, Xin Yuan 0002. Solving Inverse Problems via Auto-Encoders |
| 324 | -- | 335 | Zinan Lin 0001, Ashish Khetan, Giulia C. Fanti, Sewoong Oh. PacGAN: The Power of Two Samples in Generative Adversarial Networks |
| 336 | -- | 347 | Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan, Philip Schniter, Alyson K. Fletcher. Inference With Deep Generative Priors in High Dimensions |
| 348 | -- | 0 | . Deep Learning: Mathematical Foundations and Applications to Information Science |