Search Results for author:
Found 7 papers, 3 papers with code
Quantum Algorithms for the Pathwise Lasso
We present a novel quantum high-dimensional linear regression algorithm with an $\ell_1$-penalty based on the classical LARS (Least Angle Regression) pathwise algorithm.
Adapter Pruning using Tropical Characterization
Adapters are widely popular parameter-efficient transfer learning approaches in natural language processing that insert trainable modules in between layers of a pre-trained language model.
Abstract Visual Reasoning: An Algebraic Approach for Solving Raven's Progressive Matrices
We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning.
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
We propose a new approach, Knowledge Distillation using Optimal Transport (KNOT), to distill the natural language semantic knowledge from multiple teacher networks to a student network.
Emotion Recognition in Conversation
Knowledge Distillation
+4
Topological Mapping for Manhattan-like Repetitive Environments
At the intermediate level, the map is represented as a Manhattan Graph where the nodes and edges are characterized by Manhattan properties and as a Pose Graph at the lower-most level of detail.
Averaging plus Learning Models and Their Asymptotics
We develop original models to study interacting agents in financial markets and in social networks.
Learning Agents in Black-Scholes Financial Markets: Consensus Dynamics and Volatility Smiles
Black-Scholes (BS) is the standard mathematical model for option pricing in financial markets.
