IEEE ICASSP 2024 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The IEEE ICASSP 2024 conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world. Visit the website.
- Read more about Alleviating Hallucinations via Supportive Window Indexing in Abstractive Summarization
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Abstractive summarization models learned with maximum likelihood estimation (MLE) have been proven to produce hallucinatory content, which heavily limits their real-world
applicability. Preceding studies attribute this problem to the semantic insensitivity of MLE, and they compensate for it with additional unsupervised learning objectives that maximize the metrics of document-summary inferring, however, resulting in unstable and expensive model training. In this paper, we propose a novel supportive windows indexing
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- Read more about Iterative Autoregressive Generation for Abstractive Summarization
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Abstractive summarization suffers from exposure bias caused by the teacher-forced maximum likelihood estimation (MLE) learning, that an autoregressive language model predicts the next token distribution conditioned on the exact pre-context during training while on its own predictions at inference. Preceding resolutions for this problem straightforwardly augment the pure token-level MLE with summary-level objectives.
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- Read more about Stethoscope-Guided Supervised Contrastive Learning for Cross-domain Adaptation on Respiratory Sound Classification
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Despite the remarkable advances in deep learning technology, achieving satisfactory performance in lung sound classification remains a challenge due to the scarcity of available data. Moreover, the respiratory sound samples are collected from a variety of electronic stethoscopes, which could potentially introduce biases into the trained models. When a significant distribution shift occurs within the test dataset or in a practical scenario, it can substantially decrease the performance.
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- Read more about FINCGAN: A GAN FRAMEWORK OF IMBALANCED NODE CLASSIFICATION ON HETEROGENEOUS GRAPH NEURAL NETWORK
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We introduce FincGAN, a GAN framework designed to address the class imbalance in GNNs by enhancing minority sample synthesis and ensuring connectivity with sparsity-aware edge generators.
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- Read more about MODEL-BASED LABEL-TO-IMAGE DIFFUSION FOR SEMI-SUPERVISED CHOROIDAL VESSEL SEGMENTATION
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Current successful choroidal vessel segmentation methods rely on large amounts of voxel-level annotations on the 3D optical coherence tomography images, which are hard and time-consuming. Semi-supervised learning solves this issue by enabling model learning from both unlabeled data and a limited amount of labeled data. A challenge is the defective pseudo labels generated for the unlabeled data. In this work, we propose a model-based label-to-image diffusion (MLD) framework for semi-supervised choroidal vessel segmentation.
ICASSP_poster.pdf
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Contrastive learning has demonstrated great effectiveness in representation learning especially for image classification tasks. However, there is still a shortage in the studies targeting regression tasks, and more specifically applications on hyperspectral data. In this paper, we propose a contrastive learning framework for the regression tasks for hyperspectral data. To this end, we provide a collection of transformations relevant for augmenting hyperspectral data, and investigate contrastive learning for regression.
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- Read more about Unsupervised Acoustic Scene Mapping Based on Acoustic Features and Dimensionality Reduction
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Classical methods for acoustic scene mapping require the estimation of the time difference of arrival (TDOA) between microphones. Unfortunately, TDOA estimation is very sensitive to reverberation and additive noise. We introduce an unsupervised data-driven approach that exploits the natural structure of the data. Toward this goal, we adapt the recently proposed local conformal autoencoder (LOCA) – an offline deep learning scheme for extracting standardized data coordinates from measurements.
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- Read more about Poster for the paper "Revisiting Deep Generalized Canonical Correlation Analysis"
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Canonical correlation analysis (CCA) is a classic statistical method for discovering latent co-variation that underpins two or more observed random vectors. Several extensions and variations of CCA have been proposed that have strengthened our capabilities in terms of revealing common random factors from multiview datasets. In this work, we first revisit the most recent deterministic extensions of deep CCA and highlight the strengths and limitations of these state-of-the-art methods. Some methods allow trivial solutions, while others can miss weak common factors.
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- Read more about Poster of the paper "Multivariate Density Estimation Using Low-Rank Fejér-Riesz Factorization"
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We consider the problem of learning smooth multivariate probability density functions. We invoke the canonical decomposition of multivariate functions and we show that if a joint probability density function admits a truncated Fourier series representation, then the classical univariate Fejér-Riesz Representation Theorem can be used for learning bona fide joint probability density functions. We propose a scalable, flexible, and direct framework for learning smooth multivariate probability density functions even from potentially incomplete datasets.
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- Read more about Improving Continual Learning of Acoustic Scene Classification via Mutual Information Optimization
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Continual learning, which aims to incrementally accumulate knowledge, has been an increasingly significant but challenging research topic for deep models that are prone to catastrophic forgetting. In this paper, we propose a novel replay-based continual learning approach in the context of class-incremental learning in acoustic scene classification, to classify audio recordings into an expanding set of classes that characterize the acoustic scenes. Our approach is improving both the modeling and memory selection mechanism via mutual information optimization in continual learning.
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