Zobrazeno 1 - 10
of 173
pro vyhledávání: '"P. Thejaswi"'
Data summarization tasks are often modeled as $k$-clustering problems, where the goal is to choose $k$ data points, called cluster centers, that best represent the dataset by minimizing a clustering objective. A popular objective is to minimize the m
Externí odkaz:
http://arxiv.org/abs/2410.12913
Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label values to a subset of them, namely a prediction set, and asking them to always predict label values from
Externí odkaz:
http://arxiv.org/abs/2406.06671
Autor:
De Toni, Giovanni, Okati, Nastaran, Thejaswi, Suhas, Straitouri, Eleni, Gomez-Rodriguez, Manuel
Decision support systems based on prediction sets have proven to be effective at helping human experts solve classification tasks. Rather than providing single-label predictions, these systems provide sets of label predictions constructed using confo
Externí odkaz:
http://arxiv.org/abs/2405.17544
Resettlement agencies have started to adopt data-driven algorithmic matching to match refugees to locations using employment rate as a measure of utility. Given a pool of refugees, data-driven algorithmic matching utilizes a classifier to predict the
Externí odkaz:
http://arxiv.org/abs/2407.13052
Large language models are often ranked according to their level of alignment with human preferences -- a model is better than other models if its outputs are more frequently preferred by humans. One of the popular ways to elicit human preferences uti
Externí odkaz:
http://arxiv.org/abs/2402.17826
In this work, we study diversity-aware clustering problems where the data points are associated with multiple attributes resulting in intersecting groups. A clustering solution need to ensure that a minimum number of cluster centers are chosen from e
Externí odkaz:
http://arxiv.org/abs/2401.05502
Autor:
Neto, Jogi Suda, Deng, Li, Raya, Thejaswi, Shahbazi, Reza, Liu, Nick, Venkatesh, Adhitya, Shah, Miral, Khosla, Neeru, Guido, Rodrigo Capobianco
Language Models are being widely used in Education. Even though modern deep learning models achieve very good performance on question-answering tasks, sometimes they make errors. To avoid misleading students by showing wrong answers, it is important
Externí odkaz:
http://arxiv.org/abs/2308.03866
The problem of column subset selection asks for a subset of columns from an input matrix such that the matrix can be reconstructed as accurately as possible within the span of the selected columns. A natural extension is to consider a setting where t
Externí odkaz:
http://arxiv.org/abs/2306.04489
Autor:
Sahai, Saumya Y., Liu, Jing, Muniyappa, Thejaswi, Sathyendra, Kanthashree M., Alexandridis, Anastasios, Strimel, Grant P., McGowan, Ross, Rastrow, Ariya, Chang, Feng-Ju, Mouchtaris, Athanasios, Kunzmann, Siegfried
We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks. This architecture enables a dynamic switch for its runtime compute paths by exploi
Externí odkaz:
http://arxiv.org/abs/2304.01905
Autor:
Chang, Feng-Ju, Muniyappa, Thejaswi, Sathyendra, Kanthashree Mysore, Wei, Kai, Strimel, Grant P., McGowan, Ross
Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however,
Externí odkaz:
http://arxiv.org/abs/2303.17799