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Found 13 papers, 3 papers with code
Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures.
Federated Empirical Risk Minimization via Second-Order Method
Many convex optimization problems with important applications in machine learning are formulated as empirical risk minimization (ERM).
Certifiable Robustness for Naive Bayes Classifiers
We present (i) a linear time algorithm in the number of entries in the dataset that decides whether a test point is certifiably robust for NBC, (ii) an algorithm that counts for each label, the number of cleaned datasets on which the NBC can be trained to predict that label, and (iii) an efficient optimal algorithm that poisons a clean dataset by inserting the minimum number of missing values such that a test point is not certifiably robust for NBC.
Does compressing activations help model parallel training?
Finally, we provide insights for future development of model parallelism compression algorithms.
Virtual Secure Platform: A Five-Stage Pipeline Processor over TFHE
Our experiments show that both the pipelined architecture and the CMUX Memory technique are effective in improving the performance of the proposed processor.
Cryptography and Security
FedNNNN: Norm-Normalized Neural Network Aggregation for Fast and Accurate Federated Learning
In this work, we find out that averaging models from different clients significantly diminishes the norm of the update vectors, resulting in slow learning rate and low prediction accuracy.
BUNET: Blind Medical Image Segmentation Based on Secure UNET
The strict security requirements placed on medical records by various privacy regulations become major obstacles in the age of big data.
AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference
Prior HPPNNs over-pessimistically select huge HE parameters to maintain large noise budgets, since they use the same set of HE parameters for an entire network and ignore the error tolerance capability of a network.
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification
We propose Crowd-based neural networks for Text Sentiment Classification (CrowdTSC for short).
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition
In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of privacy-preserving visual recognition.
NASS: Optimizing Secure Inference via Neural Architecture Search
Due to increasing privacy concerns, neural network (NN) based secure inference (SI) schemes that simultaneously hide the client inputs and server models attract major research interests.
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
In this work, we propose a dissection of GNNs on graph classification into two parts: 1) the graph filtering, where graph-based neighbor aggregations are performed, and 2) the set function, where a set of hidden node features are composed for prediction.
Ranked #1 on
Graph Classification
on RE-M12K
SimGNN: A Neural Network Approach to Fast Graph Similarity Computation
Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs.
Ranked #1 on
Graph Similarity
on IMDb
