There were many great papers this past year in the field, but below (and in the video) are five papers that may have been overlooked. (YT video: https://www.youtube.com/watch?v=XnUf9twdchI)

Papers are linked as follows

Badreddine et al., "Logic Tensor Networks (journal version)," AIJ 2022 https://www.sciencedirect.com/science/article/abs/pii/S0004370221002009
Sen et al., "Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules," EMNLP 2022 https://preview.aclanthology.org/emnlp-22-ingestion/2022.emnlp-main.255.pdf
Kamienny et al., "End-to-end Symbolic Regression with Transformers," NeurIPS 2022 https://openreview.net/pdf?id=GoOuIrDHG_Y
Nandwani et al., "A Solver-Free Framework for Scalable Learning in Neural ILP Architectures," NeurIPS 2022 https://openreview.net/pdf?id=EqZuN4V_FLF
Shakarian and Simari, "Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture," TransAI 2022 https://ieeexplore.ieee.org/document/9951514

What interesting papers do you think were overlooked this past year?

Comments

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Agreeable-Run-9152 t1_j2s81ce wrote on January 3, 2023 at 4:24 PM

All of my papers

ureepamuree t1_j2w9aap wrote on January 4, 2023 at 11:23 AM

You reviewed them?

Intelligent-Aioli-43 t1_j2sg9nc wrote on January 3, 2023 at 5:16 PM

Hyperbolic Image segmentation (CVPR) was amazing

jaschau t1_j2wfnzl wrote on January 4, 2023 at 12:34 PM

Thanks for the hint! It's certainly been an overlooked paper for me. Looks interesting. Have you used it? Does it work in practice as the authors advertise it?

Intelligent-Aioli-43 t1_j2yk7mk wrote on January 4, 2023 at 9:08 PM

Yes I replicated it, works as advertised. A real bummer it's written in tf 1.x though

jaschau t1_j30oful wrote on January 5, 2023 at 6:15 AM

Thanks a lot for the feedback, I guess I'll give it a shot then. Kind of surprising to see tf 1.x in a 2022 publication, I guess I'm just glad it's not caffe :D

Intelligent-Aioli-43 t1_j33h1j5 wrote on January 5, 2023 at 8:11 PM

Xd true that!

gamerx88 t1_j2vzjfx wrote on January 4, 2023 at 9:15 AM

"An empirical analysis of compute-optimal large language model training" by Deepmind, suggesting that LLMs are over-parameterized or under-trained (insufficient data used in training).