Tom Goldstein
About
In The Last Decade
Tom Goldstein
86 papers receiving 5.2k citations
Hit Papers
Peers
Comparison fields: 5 of 152
- Computer Vision and Pattern Recognition 2.4k
- Computational Mechanics 1.7k
- Electrical and Electronic Engineering 919
- Artificial Intelligence 848
- Biomedical Engineering 738
Countries citing papers authored by Tom Goldstein
This map shows the geographic impact of Tom Goldstein's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tom Goldstein with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Goldstein more than expected).
Fields of papers citing papers by Tom Goldstein
This network shows the impact of papers produced by Tom Goldstein. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tom Goldstein. The network helps show where Tom Goldstein may publish in the future.
Co-authorship network of co-authors of Tom Goldstein
This figure shows the co-authorship network connecting the top 25 collaborators of Tom Goldstein. A scholar is included among the top collaborators of Tom Goldstein based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tom Goldstein. Tom Goldstein is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 5 | |
| 3 | 50 | |
| 4 | Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks | 1 |
| 5 | LowKey: Leveraging Adversarial Attacks to Protect Social Media Users from Facial Recognition | 38 |
| 6 | Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering | 7 |
| 7 | MetaPoison: Practical General-purpose Clean-label Data Poisoning | 3 |
| 8 | 22 | |
| 9 | Preparing for the Worst: Making Networks Less Brittle with Adversarial Batch Normalization | 2 |
| 10 | Truth or backpropaganda? An empirical investigation of deep learning theory | 2 |
| 11 | 7 | |
| 12 | Adversarially Robust Few-Shot Learning: A Meta-Learning Approach | 2 |
| 13 | Data Augmentation for Meta-Learning | 1 |
| 14 | Batch-wise Logit-Similarity: Generalizing Logit-Squeezing and Label-Smoothing. | 1 |
| 15 | Robust Few-Shot Learning with Adversarially Queried Meta-Learners | 3 |
| 16 | Automated Inference with Adaptive Batches | 23 |
| 17 | Adaptive Consensus ADMM for Distributed Optimization | 8 |
| 18 | 25 | |
| 19 | Training Quantized Nets: A Deeper Understanding | 37 |
| 20 | It's not up to you | 2 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.