1 citations · 2 across the 9 of their papers we have counts for
9 papers
Automatically Detecting Amusing Games in Wordle
Ronaldo Luo, Gary Liang, Cindy Liu +4
We explore automatically predicting which Wordle games Reddit users find amusing. We scrape approximately 80k reactions by Reddit users to Wordle games from Reddit, classify the re…
Position Information Emerges in Causal Transformers Without Positional Encodings via Similarity of Nearby Embeddings
Chunsheng Zuo, Pavel Guerzhoy, Michael Guerzhoy
Transformers with causal attention can solve tasks that require positional information without using positional encodings. In this work, we propose and investigate a new hypothesis…
Semantic, Orthographic, and Phonological Biases in Humans' Wordle Gameplay
Jiadong Liang, Adam Kabbara, Jiaying Liu +3
We show that human players' gameplay in the game of Wordle is influenced by the semantics, orthography, and phonology of the player's previous guesses. We compare actual human play…
Occam's Razor and Bender and Koller's Octopus
Michael Guerzhoy
We discuss the teaching of the discussion surrounding Bender and Koller's prominent ACL 2020 paper, "Climbing toward NLU: on meaning form, and understanding in the age of data" \ci…
Toward Learning Latent-Variable Representations of Microstructures by Optimizing in Spatial Statistics Space
Sayed Sajad Hashemi, Michael Guerzhoy, Noah H. Paulson
In Materials Science, material development involves evaluating and optimizing the internal structures of the material, generically referred to as microstructures. Microstructures s…
Breaking Symmetry When Training Transformers
Chunsheng Zuo, Michael Guerzhoy
As we show in this paper, the prediction for output token of Transformer architectures without one of the mechanisms of positional encodings and causal attention is invariant…
How Do ConvNets Understand Image Intensity?
Jackson Kaunismaa, Michael Guerzhoy
Convolutional Neural Networks (ConvNets) usually rely on edge/shape information to classify images. Visualization methods developed over the last decade confirm that ConvNets rely…
Boosting Local Spectro-Temporal Features for Speech Analysis
Michael Guerzhoy
We introduce the problem of phone classification in the context of speech recognition, and explore several sets of local spectro-temporal features that can be used for phone classi…
Salient Facial Features from Humans and Deep Neural Networks
Shanmeng Sun, Wei Zhen Teoh, Michael Guerzhoy
In this work, we explore the features that are used by humans and by convolutional neural networks (ConvNets) to classify faces. We use Guided Backpropagation (GB) to visualize the…