We perform detailed experiments to show that our method indeed increases the macro-F1 scores for attribute value extraction in general, and for labels with low training data in particular.
In this work, we provide a novel unsupervised model and inference method for learning MTPP in presence of event sequences with missing events.
Another key contribution is the study of data valuation in the domain adaptation setting, where a data value estimator obtained using checkpoints from training trajectory in the source domain training dataset is used for data valuation in a target domain training dataset.
On inspecting, we realize that an overall incentive scheme for the weak team does not incentivize the weaker agents within that team to learn and improve.
Occurrences of catastrophes such as natural or man-made disasters trigger the spread of rumours over social media at a rapid pace.
Aspect Sentiment Triplet Extraction (ASTE) deals with extracting opinion triplets, consisting of an opinion target or aspect, its associated sentiment, and the corresponding opinion term/span explaining the rationale behind the sentiment.
We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models.
In this paper, we study the problem of selecting high-value subsets of training data.
We design a novel convex optimization-based multi-criteria online subset selection algorithm that uses a thresholded concave function of selection variables.
The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc.
Training vision-based Autonomous driving models is a challenging problem with enormous practical implications.
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.
Manually extracting relevant aspects and opinions from large volumes of user-generated text is a time-consuming process.
Travel time estimation is a fundamental problem in transportation science with extensive literature.
The growth of big data in domains such as Earth Sciences, Social Networks, Physical Sciences, etc.
Distributed, Parallel, and Cluster Computing
Our objective is to devise a general methodology for customizing tests using user preferences so that expensive or uncomfortable tests can be avoided.
Experimental results on a variety of toy and real world datasets show that our approach is significantly more accurate than parameter averaging for high number of partitions.
This paper introduces kernels on attributed pointsets, which are sets of vectors embedded in an euclidean space.