pwc-archive/papers-with-abstracts huggingface dataset:
A Mechanistic Interpretation of Arithmetic Reasoning in Language Models using Causal Mediation Analysis
Authors: Alessandro Stolfo, Yonatan Belinkov, Mrinmaya Sachan
Date: 2023-05-24 00:00:00
Tasks: Arithmetic Reasoning, Mathematical Reasoning, Retrieval, Specificity

Mathematical reasoning in large language models (LMs) has garnered significant attention in recent work, but there is a limited understanding of how these models process and store information related to arithmetic tasks within their architecture. In order to improve our understanding of this aspect of language models, we present a mechanistic interpretation of Transformer-based LMs on arithmetic questions using a causal mediation analysis framework. By intervening on the activations of specific model components and measuring the resulting changes in predicted probabilities, we identify the subset of parameters responsible for specific predictions. This provides insights into how information related to arithmetic is processed by LMs. Our experimental results indicate that LMs process the input by transmitting the information relevant to the query from mid-sequence early layers to the final token using the attention mechanism. Then, this information is processed by a set of MLP modules, which generate result-related information that is incorporated into the residual stream. To assess the specificity of the observed activation dynamics, we compare the effects of different model components on arithmetic queries with other tasks, including number retrieval from prompts and factual knowledge questions.
Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Authors: Beomsu Kim, Gihyun Kwon, Kwanyoung Kim, Jong Chul Ye
Date: 2023-05-24 00:00:00
Tasks: Image-to-Image Translation, Translation

Diffusion models are a powerful class of generative models which simulate stochastic differential equations (SDEs) to generate data from noise. While diffusion models have achieved remarkable progress, they have limitations in unpaired image-to-image (I2I) translation tasks due to the Gaussian prior assumption. Schr\"{o}dinger Bridge (SB), which learns an SDE to translate between two arbitrary distributions, have risen as an attractive solution to this problem. Yet, to our best knowledge, none of SB models so far have been successful at unpaired translation between high-resolution images. In this work, we propose Unpaired Neural Schr\"{o}dinger Bridge (UNSB), which expresses the SB problem as a sequence of adversarial learning problems. This allows us to incorporate advanced discriminators and regularization to learn a SB between unpaired data. We show that UNSB is scalable and successfully solves various unpaired I2I translation tasks. Code: \url{https://github.com/cyclomon/UNSB}
C-STS: Conditional Semantic Textual Similarity
Authors: Ameet Deshpande, Carlos E. Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, Karthik Narasimhan
Date: 2023-05-24 00:00:00
Tasks: Information Retrieval, Language Model Evaluation, Language Modelling, Natural Language Understanding, Question Answering, Retrieval, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Similarity, STS

Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information retrieval and natural language understanding. However, sentence similarity can be inherently ambiguous, depending on the specific aspect of interest. We resolve this ambiguity by proposing a novel task called Conditional STS (C-STS) which measures sentences' similarity conditioned on an feature described in natural language (hereon, condition). As an example, the similarity between the sentences "The NBA player shoots a three-pointer." and "A man throws a tennis ball into the air to serve." is higher for the condition "The motion of the ball" (both upward) and lower for "The size of the ball" (one large and one small). C-STS's advantages are two-fold: (1) it reduces the subjectivity and ambiguity of STS and (2) enables fine-grained language model evaluation through diverse natural language conditions. We put several state-of-the-art models to the test, and even those performing well on STS (e.g. SimCSE, Flan-T5, and GPT-4) find C-STS challenging; all with Spearman correlation scores below 50. To encourage a more comprehensive evaluation of semantic similarity and natural language understanding, we make nearly 19K C-STS examples and code available for others to train and test their models.
Reconstruction, forecasting, and stability of chaotic dynamics from partial data
Authors: Elise Özalp, Georgios Margazoglou, Luca Magri
Date: 2023-05-24 00:00:00

The forecasting and computation of the stability of chaotic systems from partial observations are tasks for which traditional equation-based methods may not be suitable. In this computational paper, we propose data-driven methods to (i) infer the dynamics of unobserved (hidden) chaotic variables (full-state reconstruction); (ii) time forecast the evolution of the full state; and (iii) infer the stability properties of the full state. The tasks are performed with long short-term memory (LSTM) networks, which are trained with observations (data) limited to only part of the state: (i) the low-to-high resolution LSTM (LH-LSTM), which takes partial observations as training input, and requires access to the full system state when computing the loss; and (ii) the physics-informed LSTM (PI-LSTM), which is designed to combine partial observations with the integral formulation of the dynamical system's evolution equations. First, we derive the Jacobian of the LSTMs. Second, we analyse a chaotic partial differential equation, the Kuramoto-Sivashinsky (KS), and the Lorenz-96 system. We show that the proposed networks can forecast the hidden variables, both time-accurately and statistically. The Lyapunov exponents and covariant Lyapunov vectors, which characterize the stability of the chaotic attractors, are correctly inferred from partial observations. Third, the PI-LSTM outperforms the LH-LSTM by successfully reconstructing the hidden chaotic dynamics when the input dimension is smaller or similar to the Kaplan-Yorke dimension of the attractor. This work opens new opportunities for reconstructing the full state, inferring hidden variables, and computing the stability of chaotic systems from partial data.
Topic-Guided Self-Introduction Generation for Social Media Users
Authors: Chunpu Xu, Jing Li, Piji Li, Min Yang
Date: 2023-05-24 00:00:00
Tasks: Decoder, Sentence

Millions of users are active on social media. To allow users to better showcase themselves and network with others, we explore the auto-generation of social media self-introduction, a short sentence outlining a user's personal interests. While most prior work profiles users with tags (e.g., ages), we investigate sentence-level self-introductions to provide a more natural and engaging way for users to know each other. Here we exploit a user's tweeting history to generate their self-introduction. The task is non-trivial because the history content may be lengthy, noisy, and exhibit various personal interests. To address this challenge, we propose a novel unified topic-guided encoder-decoder (UTGED) framework; it models latent topics to reflect salient user interest, whose topic mixture then guides encoding a user's history and topic words control decoding their self-introduction. For experiments, we collect a large-scale Twitter dataset, and extensive results show the superiority of our UTGED to the advanced encoder-decoder models without topic modeling.
Reliability Scores from Saliency Map Clusters for Improved Image-based Harvest-Readiness Prediction in Cauliflower
Authors: Jana Kierdorf, Ribana Roscher
Date: 2023-05-24 00:00:00
Tasks: Decision Making, Interpretable Machine Learning

Cauliflower is a hand-harvested crop that must fulfill high-quality standards in sales making the timing of harvest important. However, accurately determining harvest-readiness can be challenging due to the cauliflower head being covered by its canopy. While deep learning enables automated harvest-readiness estimation, errors can occur due to field-variability and limited training data. In this paper, we analyze the reliability of a harvest-readiness classifier with interpretable machine learning. By identifying clusters of saliency maps, we derive reliability scores for each classification result using knowledge about the domain and the image properties. For unseen data, the reliability can be used to (i) inform farmers to improve their decision-making and (ii) increase the model prediction accuracy. Using RGB images of single cauliflower plants at different developmental stages from the GrowliFlower dataset, we investigate various saliency mapping approaches and find that they result in different quality of reliability scores. With the most suitable interpretation tool, we adjust the classification result and achieve a 15.72% improvement of the overall accuracy to 88.14% and a 15.44% improvement of the average class accuracy to 88.52% for the GrowliFlower dataset.
Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinement
Authors: Zhangyang Gao, Cheng Tan, Stan Z. Li
Date: 2023-05-20 00:00:00
Tasks: Protein Design, Retrieval, Word Sense Disambiguation

Recent studies have shown competitive performance in protein design that aims to find the amino acid sequence folding into the desired structure. However, most of them disregard the importance of predictive confidence, fail to cover the vast protein space, and do not incorporate common protein knowledge. After witnessing the great success of pretrained models on diverse protein-related tasks and the fact that recovery is highly correlated with confidence, we wonder whether this knowledge can push the limits of protein design further. As a solution, we propose a knowledge-aware module that refines low-quality residues. We also introduce a memory-retrieval mechanism to save more than 50\% of the training time. We extensively evaluate our proposed method on the CATH, TS50, and TS500 datasets and our results show that our Knowledge-Design method outperforms the previous PiFold method by approximately 9\% on the CATH dataset. Specifically, Knowledge-Design is the first method that achieves 60+\% recovery on CATH, TS50 and TS500 benchmarks. We also provide additional analysis to demonstrate the effectiveness of our proposed method. The code will be publicly available.
MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing
Authors: Zhangyang Gao, Xingran Chen, Cheng Tan, Stan Z. Li
Date: 2023-05-20 00:00:00
Tasks: Retrosynthesis

Is there a unified framework for graph-based retrosynthesis prediction? Through analysis of full-, semi-, and non-template retrosynthesis methods, we discovered that they strive to strike an optimal balance between combinability and consistency: \textit{Should atoms be combined as motifs to simplify the molecular editing process, or should motifs be broken down into atoms to reduce the vocabulary and improve predictive consistency?} Recent works have studied several specific cases, while none of them explores different combinability-consistency trade-offs. Therefore, we propose MotifRetro, a dynamic motif editing framework for retrosynthesis prediction that can explore the entire trade-off space and unify graph-based models. MotifRetro comprises two components: RetroBPE, which controls the combinability-consistency trade-off, and a motif editing model, where we introduce a novel LG-EGAT module to dynamiclly add motifs to the molecule. We conduct extensive experiments on USPTO-50K to explore how the trade-off affects the model performance and finally achieve state-of-the-art performance.
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
Authors: Yuanfeng Ji, Yatao Bian, Guoji Fu, Peilin Zhao, Ping Luo
Date: 2023-05-23 00:00:00

The regulation of various cellular processes heavily relies on the protein complexes within a living cell, necessitating a comprehensive understanding of their three-dimensional structures to elucidate the underlying mechanisms. While neural docking techniques have exhibited promising outcomes in binary protein docking, the application of advanced neural architectures to multimeric protein docking remains uncertain. This study introduces SyNDock, an automated framework that swiftly assembles precise multimeric complexes within seconds, showcasing performance that can potentially surpass or be on par with recent advanced approaches. SyNDock possesses several appealing advantages not present in previous approaches. Firstly, SyNDock formulates multimeric protein docking as a problem of learning global transformations to holistically depict the placement of chain units of a complex, enabling a learning-centric solution. Secondly, SyNDock proposes a trainable two-step SE(3) algorithm, involving initial pairwise transformation and confidence estimation, followed by global transformation synchronization. This enables effective learning for assembling the complex in a globally consistent manner. Lastly, extensive experiments conducted on our proposed benchmark dataset demonstrate that SyNDock outperforms existing docking software in crucial performance metrics, including accuracy and runtime. For instance, it achieves a 4.5% improvement in performance and a remarkable millionfold acceleration in speed.
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Date: 2023-05-24 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Gaussian Processes

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular solution concept of Shapley values extended to stochastic cooperative games, resulting in explanations that are random variables. The GP explanations generated using our approach satisfy similar favorable axioms to standard Shapley values and possess a tractable covariance function across features and data observations. This covariance allows for quantifying explanation uncertainties and studying the statistical dependencies between explanations. We further extend our framework to the problem of predictive explanation, and propose a Shapley prior over the explanation function to predict Shapley values for new data based on previously computed ones. Our extensive illustrations demonstrate the effectiveness of the proposed approach.
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Authors: Jan Achterhold, Philip Tobuschat, Hao Ma, Dieter Buechler, Michael Muehlebach, Joerg Stueckler
Date: 2023-05-24 00:00:00
Tasks: Prediction, Trajectory Prediction

In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to learn parameters of the dynamics model, of an extended Kalman filter, and of a neural model that infers the ball's initial condition. We demonstrate superior prediction performance of our approach over two black-box approaches, which are not supplied with physical prior knowledge. We demonstrate that initializing the spin from parameters of the ball launcher using a neural network drastically improves long-time prediction performance over estimating the spin purely from measured ball positions. An accurate prediction of the ball trajectory is crucial for successful returns. We therefore evaluate the return performance with a pneumatic artificial muscular robot and achieve a return rate of 29/30 (97.7%).
DiffBlender: Scalable and Composable Multimodal Text-to-Image Diffusion Models
Authors: Sungnyun Kim, Junsoo Lee, Kibeom Hong, Daesik Kim, Namhyuk Ahn
Date: 2023-05-24 00:00:00
Tasks: Conditional Image Generation, Image Generation, multimodal generation, Text-to-Image Generation

In this study, we aim to extend the capabilities of diffusion-based text-to-image (T2I) generation models by incorporating diverse modalities beyond textual description, such as sketch, box, color palette, and style embedding, within a single model. We thus design a multimodal T2I diffusion model, coined as DiffBlender, by separating the channels of conditions into three types, i.e., image forms, spatial tokens, and non-spatial tokens. The unique architecture of DiffBlender facilitates adding new input modalities, pioneering a scalable framework for conditional image generation. Notably, we achieve this without altering the parameters of the existing generative model, Stable Diffusion, only with updating partial components. Our study establishes new benchmarks in multimodal generation through quantitative and qualitative comparisons with existing conditional generation methods. We demonstrate that DiffBlender faithfully blends all the provided information and showcase its various applications in the detailed image synthesis.
Real time dense anomaly detection by learning on synthetic negative data
Authors: Anja Delić, Matej Grcić, Siniša Šegvić
Date: 2023-05-24 00:00:00
Tasks: Anomaly Detection

Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes the same shared representation according to cross-entropy of the discriminative predictions, and negative log likelihood of the predicted energy-based density. We extend that work with a jointly trained generative flow that samples synthetic negatives at the border of the inlier distribution. The proposed extension provides potential to learn the hybrid method without real negative data. Our experiments analyze the impact of training with synthetic negative data and validate contribution of the energy-based density during training and evaluation.
Deep Learning and Ethics
Authors: Travis LaCroix, Simon J. D. Prince
Date: 2023-05-24 00:00:00
Tasks: Deep Learning, Ethics, Philosophy

This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations, militarization, fraud, and environmental concerns. The aim is not to provide advice on being more ethical. Instead, the goal is to express ideas and start conversations in key areas that have received attention in philosophy, political science, and the broader social sciences.
Robust Classification via a Single Diffusion Model
Authors: Huanran Chen, Yinpeng Dong, Zhengyi Wang, Xiao Yang, Chengqi Duan, Hang Su, Jun Zhu
Date: 2023-05-24 00:00:00
Tasks: Adversarial Defense, Adversarial Robustness, Classification, model, Robust classification

Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training. However, diffusion-based purification can be evaded by stronger adaptive attacks while adversarial training does not perform well under unseen threats, exhibiting inevitable limitations of these methods. To better harness the expressive power of diffusion models, this paper proposes Robust Diffusion Classifier (RDC), a generative classifier that is constructed from a pre-trained diffusion model to be adversarially robust. RDC first maximizes the data likelihood of a given input and then predicts the class probabilities of the optimized input using the conditional likelihood estimated by the diffusion model through Bayes' theorem. To further reduce the computational cost, we propose a new diffusion backbone called multi-head diffusion and develop efficient sampling strategies. As RDC does not require training on particular adversarial attacks, we demonstrate that it is more generalizable to defend against multiple unseen threats. In particular, RDC achieves $75.67\%$ robust accuracy against various $\ell_\infty$ norm-bounded adaptive attacks with $\epsilon_\infty=8/255$ on CIFAR-10, surpassing the previous state-of-the-art adversarial training models by $+4.77\%$. The results highlight the potential of generative classifiers by employing pre-trained diffusion models for adversarial robustness compared with the commonly studied discriminative classifiers. Code is available at \url{https://github.com/huanranchen/DiffusionClassifier}.
Machine Unlearning: its nature, scope, and importance for a "delete culture"
Authors: Luciano Floridi
Date: 2023-05-24 00:00:00
Tasks: Blocking, Machine Unlearning

The article explores the cultural shift from recording to deleting information in the digital age and its implications on privacy, intellectual property (IP), and Large Language Models like ChatGPT. It begins by defining a delete culture where information, in principle legal, is made unavailable or inaccessible because unacceptable or undesirable, especially but not only due to its potential to infringe on privacy or IP. Then it focuses on two strategies in this context: deleting, to make information unavailable; and blocking, to make it inaccessible. The article argues that both strategies have significant implications, particularly for machine learning (ML) models where information is not easily made unavailable. However, the emerging research area of Machine Unlearning (MU) is highlighted as a potential solution. MU, still in its infancy, seeks to remove specific data points from ML models, effectively making them 'forget' completely specific information. If successful, MU could provide a feasible means to manage the overabundance of information and ensure a better protection of privacy and IP. However, potential ethical risks, such as misuse, overuse, and underuse of MU, should be systematically studied to devise appropriate policies.
Attention to Mean-Fields for Particle Cloud Generation
Authors: Benno Käch, Isabell Melzer-Pellmann
Date: 2023-05-24 00:00:00

The generation of collider data using machine learning has emerged as a prominent research topic in particle physics due to the increasing computational challenges associated with traditional Monte Carlo simulation methods, particularly for future colliders with higher luminosity. Although generating particle clouds is analogous to generating point clouds, accurately modelling the complex correlations between the particles presents a considerable challenge. Additionally, variable particle cloud sizes further exacerbate these difficulties, necessitating more sophisticated models. In this work, we propose a novel model that utilizes an attention-based aggregation mechanism to address these challenges. The model is trained in an adversarial training paradigm, ensuring that both the generator and critic exhibit permutation equivariance/invariance with respect to their input. A novel feature matching loss in the critic is introduced to stabilize the training. The proposed model performs competitively to the state-of-art whilst having significantly fewer parameters.