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Found 11 papers, 1 papers with code
Context-Aware Clustering using Large Language Models
Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities.
Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking
Through extensive experiments on CDL-B channel model, we show that the HKF can be used for tracking the channel over a wide range of Doppler values, matching Kalman filter performance with genie Doppler information.
A White Paper on Neural Network Quantization
Neural network quantization is one of the most effective ways of achieving these savings but the additional noise it induces can lead to accuracy degradation.
Bayesian Bits: Unifying Quantization and Pruning
We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization.
Up or Down? Adaptive Rounding for Post-Training Quantization
In this paper, we propose AdaRound, a better weight-rounding mechanism for post-training quantization that adapts to the data and the task loss.
Class-Conditional Compression and Disentanglement: Bridging the Gap between Neural Networks and Naive Bayes Classifiers
We furthermore suggest a neural network where the decoder architecture is a parameterized naive Bayes decoder.
Understanding Neural Networks and Individual Neuron Importance via Information-Ordered Cumulative Ablation
In this work, we investigate the use of three information-theoretic quantities -- entropy, mutual information with the class variable, and a class selectivity measure based on Kullback-Leibler divergence -- to understand and study the behavior of already trained fully-connected feed-forward neural networks.
Extended Affinity Propagation: Global Discovery and Local Insights
We propose a new clustering algorithm, Extended Affinity Propagation, based on pairwise similarities.
Learning Representations for Neural Network-Based Classification Using the Information Bottleneck Principle
In this theory paper, we investigate training deep neural networks (DNNs) for classification via minimizing the information bottleneck (IB) functional.
Co-Clustering via Information-Theoretic Markov Aggregation
We present an information-theoretic cost function for co-clustering, i. e., for simultaneous clustering of two sets based on similarities between their elements.
Hard Clusters Maximize Mutual Information
In this paper, we investigate mutual information as a cost function for clustering, and show in which cases hard, i. e., deterministic, clusters are optimal.
