2 citations · 2 across the 5 of their papers we have counts for
7 papers
Large Language Models Understanding: an Inherent Ambiguity Barrier
Daniel N. Nissani
A lively ongoing debate is taking place, since the extraordinary emergence of Large Language Models (LLMs) with regards to their capability to understand the world and capture the…
Contrastive Learning and Abstract Concepts: The Case of Natural Numbers
Daniel N. Nissani
Contrastive Learning (CL) has been successfully applied to classification and other downstream tasks related to concrete concepts, such as objects contained in the ImageNet dataset…
Contrastive Learning and the Emergence of Attributes Associations
Daniel N. Nissani
In response to an object presentation, supervised learning schemes generally respond with a parsimonious label. Upon a similar presentation we humans respond again with a label, bu…
Tensions Between the Proxies of Human Values in AI
Teresa Datta, Daniel Nissani, Max Cembalest +3
Motivated by mitigating potentially harmful impacts of technologies, the AI community has formulated and accepted mathematical definitions for certain pillars of accountability: e.…
A Simple Generative Network
Daniel N. Nissani
Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were p…
Unsupervisedly Learned Representations: Should the Quest be Over?
Daniel N. Nissani
After four decades of research there still exists a Classification accuracy gap of about 20% between our best Unsupervisedly Learned Representations methods and the accuracy rates…
An Unsupervised Learning Classifier with Competitive Error Performance
Daniel N. Nissani
An unsupervised learning classification model is described. It achieves classification error probability competitive with that of popular supervised learning classifiers such as SV…