2 citations · 2 across the 3 of their papers we have counts for
8 papers
Efficient Few-Shot Continual Learning in Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision-language models (VLMs) excel in tasks such as visual question answering and image captioning. However, VLMs are often limited by their use of pretrained image encoders, like…
Decomposable Transformer Point Processes
Aristeidis Panos
The standard paradigm of modeling marked point processes is by parameterizing the intensity function using an attention-based (Transformer-style) architecture. Despite the flexibil…
Imperfect Vision Encoders: Efficient and Robust Tuning for Vision-Language Models
Aristeidis Panos, Rahaf Aljundi, Daniel Olmeda Reino +1
Vision language models (VLMs) demonstrate impressive capabilities in visual question answering and image captioning, acting as a crucial link between visual and language models. Ho…
First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning
Aristeidis Panos, Yuriko Kobe, Daniel Olmeda Reino +2
In Class-Incremental Learning (CIL) an image classification system is exposed to new classes in each learning session and must be updated incrementally. Methods approaching this pr…
How Good are Low-Rank Approximations in Gaussian Process Regression?
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on trun…
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
Aristeidis Panos, Ioannis Kosmidis, Petros Dellaportas
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to der…
How Good are Low-Rank Approximations in Gaussian Process Regression?
Constantinos Daskalakis, Petros Dellaportas, Aristeidis Panos
We provide guarantees for approximate Gaussian Process (GP) regression resulting from two common low-rank kernel approximations: based on random Fourier features, and based on trun…
Fully Scalable Gaussian Processes using Subspace Inducing Inputs
Aristeidis Panos, Petros Dellaportas, Michalis K. Titsias
We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional inp…