46 citations · 160 across the 23 of their papers we have counts for
7 papers · 1 filter
À-la-carte Prompt Tuning (APT): Combining Distinct Data Via Composable Prompting
Benjamin Bowman, Alessandro Achille, Luca Zancato +4
We introduce À-la-carte Prompt Tuning (APT), a transformer-based scheme to tune prompts on distinct data so that they can be arbitrarily composed at inference time. The individual…
Stacked Residuals of Dynamic Layers for Time Series Anomaly Detection
L. Zancato, A. Achille, G. Paolini +2
We present an end-to-end differentiable neural network architecture to perform anomaly detection in multivariate time series by incorporating a Sequential Probability Ratio Test on…
DIVA: Dataset Derivative of a Learning Task
Yonatan Dukler, Alessandro Achille, Giovanni Paolini +3
We present a method to compute the derivative of a learning task with respect to a dataset. A learning task is a function from a training set to the validation error, which can be…
Estimating informativeness of samples with Smooth Unique Information
Hrayr Harutyunyan, Alessandro Achille, Giovanni Paolini +4
We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final w…
Structured Prediction as Translation between Augmented Natural Languages
Giovanni Paolini, Ben Athiwaratkun, Jason Krone +6
We propose a new framework, Translation between Augmented Natural Languages (TANL), to solve many structured prediction language tasks including joint entity and relation extractio…
Where is the Information in a Deep Neural Network?
Alessandro Achille, Giovanni Paolini, Stefano Soatto
Whatever information a deep neural network has gleaned from training data is encoded in its weights. How this information affects the response of the network to future data remains…
The Information Complexity of Learning Tasks, their Structure and their Distance
Alessandro Achille, Giovanni Paolini, Glen Mbeng +1
We introduce an asymmetric distance in the space of learning tasks, and a framework to compute their complexity. These concepts are foundational for the practice of transfer learni…