no code implementations • LREC 2022 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks.
Ranked #1 on Fake News Detection on PolitiFact
no code implementations • 17 Oct 2023 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao, Per-Arne Andersen, Svein Anders Tunheim, Rishad Shafik, Alex Yakovlev
In brief, the TA of each clause literal has both an absorbing Exclude- and an absorbing Include state, making the learning scheme absorbing instead of ergodic.
1 code implementation • 25 Mar 2023 • Emilia Przybysz, Bimal Bhattarai, Cosimo Persia, Ana Ozaki, Ole-Christoffer Granmo, Jivitesh Sharma
Then, we show the correctness of our encoding and provide results for the properties: adversarial robustness, equivalence, and similarity of TsMs.
no code implementations • 19 Jan 2023 • K. Darshana Abeyrathna, Ahmed Abdulrahem Othman Abouzeid, Bimal Bhattarai, Charul Giri, Sondre Glimsdal, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Jivitesh Sharma, Svein Anders Tunheim, Xuan Zhang
This paper introduces a novel variant of TM learning - Clause Size Constrained TMs (CSC-TMs) - where one can set a soft constraint on the clause size.
1 code implementation • 2 Jan 2023 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao, Rohan Yadav, Jivitesh Sharma
We also visualize word clusters in vector space, demonstrating how our logical embedding co-locate similar words.
5 code implementations • LREC 2022 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.
5 code implementations • 10 May 2021 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses.
5 code implementations • 17 Nov 2020 • Bimal Bhattarai, Ole-Christoffer Granmo, Lei Jiao
The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data.
2 code implementations • 10 Sep 2020 • K. Darshana Abeyrathna, Bimal Bhattarai, Morten Goodwin, Saeed Gorji, Ole-Christoffer Granmo, Lei Jiao, Rupsa Saha, Rohan K. Yadav
We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy.