Search Results for author:
Found 16 papers, 11 papers with code
Goal-oriented Communications based on Recursive Early Exit Neural Networks
This paper presents a novel framework for goal-oriented semantic communications leveraging recursive early exit models.
Adaptive Layer Selection for Efficient Vision Transformer Fine-Tuning
To this end, in this paper we introduce an efficient fine-tuning method for ViTs called $\textbf{ALaST}$ ($\textit{Adaptive Layer Selection Fine-Tuning for Vision Transformers}$) to speed up the fine-tuning process while reducing computational cost, memory load, and training time.
Joint or Disjoint: Mixing Training Regimes for Early-Exit Models
Early exits are an important efficiency mechanism integrated into deep neural networks that allows for the termination of the network's forward pass before processing through all its layers.
Adaptive Semantic Token Selection for AI-native Goal-oriented Communications
In this paper, we propose a novel design for AI-native goal-oriented communications, exploiting transformer neural networks under dynamic inference constraints on bandwidth and computation.
Conditional computation in neural networks: principles and research trends
This article summarizes principles and ideas from the emerging area of applying \textit{conditional computation} methods to the design of neural networks.
Class incremental learning with probability dampening and cascaded gated classifier
The latter is a gated incremental classifier, helping the model modify past predictions without directly interfering with them.
NACHOS: Neural Architecture Search for Hardware Constrained Early Exit Neural Networks
To this end, this work presents Neural Architecture Search for Hardware Constrained Early Exit Neural Networks (NACHOS), the first NAS framework for the design of optimal EENNs satisfying constraints on the accuracy and the number of Multiply and Accumulate (MAC) operations performed by the EENNs at inference time.
Centroids Matching: an efficient Continual Learning approach operating in the embedding space
In this paper, we propose a novel regularization method called Centroids Matching, that, inspired by meta-learning approaches, fights CF by operating in the feature space produced by the neural network, achieving good results while requiring a small memory footprint.
Continual Learning with Invertible Generative Models
We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
Pixle: a fast and effective black-box attack based on rearranging pixels
Recent research has found that neural networks are vulnerable to several types of adversarial attacks, where the input samples are modified in such a way that the model produces a wrong prediction that misclassifies the adversarial sample.
Structured Ensembles: an Approach to Reduce the Memory Footprint of Ensemble Methods
In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches.
Avalanche: an End-to-End Library for Continual Learning
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning.
Pseudo-Rehearsal for Continual Learning with Normalizing Flows
We show that our method performs favorably with respect to state-of-the-art approaches in the literature, with bounded computational power and memory overheads.
Bayesian Neural Networks With Maximum Mean Discrepancy Regularization
Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e. g., interpretability, multi-task learning, and calibration.
DeepRICH: Learning Deeply Cherenkov Detectors
Imaging Cherenkov detectors are largely used for particle identification (PID) in nuclear and particle physics experiments, where developing fast reconstruction algorithms is becoming of paramount importance to allow for near real time calibration and data quality control, as well as to speed up offline analysis of large amount of data.
Efficient Continual Learning in Neural Networks with Embedding Regularization
Continual learning of deep neural networks is a key requirement for scaling them up to more complex applicative scenarios and for achieving real lifelong learning of these architectures.
