6 citations · 16 across the 11 of their papers we have counts for
11 papers
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
Iordanis Fostiropoulos, Muhammad Rafay Azhar, Abdalaziz Sawwan +8
We introduce GISTBench, a benchmark for evaluating Large Language Models' (LLMs) ability to understand users from their interaction histories in recommendation systems. Unlike trad…
Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods
Panos Achlioptas, Alexandros Benetatos, Iordanis Fostiropoulos +1
In this work, we systematically study the problem of personalized text-to-image generation, where the output image is expected to portray information about specific human subjects.…
Batch Model Consolidation: A Multi-Task Model Consolidation Framework
Iordanis Fostiropoulos, Jiaye Zhu, Laurent Itti
In Continual Learning (CL), a model is required to learn a stream of tasks sequentially without significant performance degradation on previously learned tasks. Current approaches…
Lightweight Learner for Shared Knowledge Lifelong Learning
Yunhao Ge, Yuecheng Li, Di Wu +12
In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicat…
Reproducibility Requires Consolidated Artifacts
Iordanis Fostiropoulos, Bowman Brown, Laurent Itti
Machine learning is facing a 'reproducibility crisis' where a significant number of works report failures when attempting to reproduce previously published results. We evaluate the…
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network
Iordanis Fostiropoulos, Laurent Itti
Transformations in the input space of Deep Neural Networks (DNN) lead to unintended changes in the feature space. Almost perceptually identical inputs, such as adversarial examples…
Implicit Feature Decoupling with Depthwise Quantization
Iordanis Fostiropoulos, Barry Boehm
Quantization has been applied to multiple domains in Deep Neural Networks (DNNs). We propose Depthwise Quantization (DQ) where is applied to a decomposed su…
Graph Conditioned Sparse-Attention for Improved Source Code Understanding
Junyan Cheng, Iordanis Fostiropoulos, Barry Boehm
Transformer architectures have been successfully used in learning source code representations. The fusion between a graph representation like Abstract Syntax Tree (AST) and a sourc…
GN-Transformer: Fusing Sequence and Graph Representation for Improved Code Summarization
Junyan Cheng, Iordanis Fostiropoulos, Barry Boehm
As opposed to natural languages, source code understanding is influenced by grammatical relationships between tokens regardless of their identifier name. Graph representations of s…
Learning Hyperbolic Representations of Topological Features
Panagiotis Kyriakis, Iordanis Fostiropoulos, Paul Bogdan
Learning task-specific representations of persistence diagrams is an important problem in topological data analysis and machine learning. However, current state of the art methods…
Depthwise Discrete Representation Learning
Iordanis Fostiropoulos
Recent advancements in learning Discrete Representations as opposed to continuous ones have led to state of art results in tasks that involve Language, Audio and Vision. Some laten…