Learning Discriminative Representations and Decision Boundaries for Open Intent Detection

Discriminative learning for neural decision feedback equalizers

In this work new Decision-Feedback (DF) Neural Equalizers (DFNE) are introduced and compared with classical DF equalizers and Viterbi demodulators. It is shown that the choice of an innovative cost functional based on the Discriminative Learning (DL) technique, coupled with a fast training paradigm, can provide neural equalizers that outperform standard DF equalizers (DFEs) at practical signal ...

Large-scale learning of discriminative image representations

Discriminative Learning over Constrained Latent Representations

This paper proposes a general learning framework for a class of problems that require learning over latent intermediate representations. Many natural language processing (NLP) decision problems are defined over an expressive intermediate representation that is not explicit in the input, leaving the algorithm with both the task of recovering a good intermediate representation and learning to cla...

Learning Discriminative Representations for Semantic Cross Media Retrieval

Heterogeneous gap among different modalities emerges as one of the critical issues in modern AI problems. Unlike traditional uni-modal cases, where raw features are extracted and directly measured, the heterogeneous nature of cross modal tasks requires the intrinsic semantic representation to be compared in a unified framework. This paper studies the learning of different representations that c...

Learning Discriminative Distance Functions for Case Retrieval and Decision Support

The importance of learning distance functions is gradually being acknowledged by the machine learning community, and different techniques are suggested that can successfully learn a strong distance function in many various contexts. Nevertheless the studies in the area are still rather fragmentary; they lack systematic analysis and focus on a limited circle of application domains. In this paper...