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Applications · Chemistry and Drug Discovery

Anton Bushuiev, Roman Bushuiev, Petr Kouba, Anatolii Filkin, Markéta Gabrielová, Michal Gabriel, Jiri Sedlar, Tomas Pluskal, Jiri Damborsky, Stanislav Mazurenko 等

Discovering mutations enhancing protein-protein interactions (PPIs) is critical for advancing biomedical research and developing improved therapeutics. While machine learning approaches have substantially advanced the field, they often struggle to generalize beyond training data in practical scenarios. The contributions of this work are three-fold. First, we construct PPIRef, the largest and non-redundant dataset of 3D protein-protein interactions, enabling effective large-scale learning. Second, we leverage the PPIRef dataset to pre-train PPIformer, a new SE(3)-equivariant model generalizing across diverse protein-binder variants. We fine-tune PPIformer to predict effects of mutations on protein-protein interactions via a thermodynamically motivated adjustment of the pre-training loss function. Finally, we demonstrate the enhanced generalization of our new PPIformer approach by outperforming other state-of-the-art methods on new, non-leaking splits of standard labeled PPI mutational data and independent case studies optimizing a human antibody against SARS-CoV-2 and increasing the thrombolytic activity of staphylokinase.

Social Aspects

Seamus Somerstep, Yuekai Sun, Yaacov Ritov

Motivated by equilibrium models of labor markets, we develop a formulation of causal strategic classification in which strategic agents can directly manipulate their outcomes. As an application, we consider employers that seek to anticipate the strategic response of a labor force when developing a hiring policy. We show theoretically that employers with performatively optimal hiring policies improve employer reward, labor force skill level, and labor force equity (compared to employers that do not anticipate the strategic labor force response) in the classic Coate-Loury labor market model. Empirically, we show that these desirable properties of performative hiring policies do generalize to our own formulation of a general equilibrium labor market. On the other hand, we also observe that the benefits of performatively optimal hiring policies are brittle in some aspects. We demonstrate that in our formulation a performative employer both harms workers by reducing their aggregate welfare and fails to prevent discrimination when more sophisticated wage and cost structures are introduced.

Miscellaneous Aspects of Machine Learning · Unsupervised and Semi-supervised Learning

Prakhar Kaushik, Aayush Mishra, Adam Kortylewski, Alan Yuille

We consider the problem of source-free unsupervised category-level 3D pose estimation from only RGB images to an non-annotated and unlabelled target domain without any access to source domain data or annotations during adaptation. Collecting and annotating real world 3D data and corresponding images is laborious, expensive yet unavoidable process since even 3D pose domain adaptation methods require 3D data in the target domain. We introduce a method which is capable of adapting to a nuisance ridden target domain without any 3D data or annotations. We represent object categories as simple cuboid meshes, and harness a generative model of neural feature activations modeled as a von Mises Fisher distribution at each mesh vertex learnt using differential rendering. We focus on individual mesh vertex features and iteratively update them based on their proximity to corresponding features in the target domain. Our key insight stems from the observation that specific object subparts remain stable across out-of-domain (OOD) scenarios, enabling strategic utilization of these invariant subcomponents for effective model updates. Our model is then trained in an EM fashion alternating between updating the vertex features and feature extractor. We show that our method simulates fine-tuning on a global-pseudo labelled dataset under mild assumptions which converges to the target domain asymptotically. Through extensive empirical validation, we demonstrate the potency of our simple approach in addressing the domain shift challenge and significantly enhancing pose estimation accuracy. By accentuating robust and less changed object subcomponents, our framework contributes to the evolution of UDA techniques in the context of 3D pose estimation using only images from the target domain.

Reinforcement Learning

Sobhan Mohammadpour, Emma Frejinger, Pierre-Luc Bacon

Regularized reinforcement learning (RL), particularly the entropy-regularized kind, has gained traction in optimal control and inverse RL. While standard unregularized RL methods remain unaffected by changes in the number of actions, we show that it can severely impact their regularized counterparts. This paper demonstrates the importance of decoupling the regularizer from the action space: that is, to maintain a consistent level of regularization regardless of how many actions are involved to avoid over-regularization. Whereas the problem can be avoided by introducing a task-specific temperature parameter, it is often undesirable and cannot solve the problem when action spaces are state-dependent. In the state-dependent action context, different states with varying action spaces are regularized inconsistently. We introduce two solutions: a static temperature selection approach and a dynamic counterpart, universally applicable where this problem arises. Implementing these changes improves performance on the DeepMind control suite in static and dynamic temperature regimes and a biological design task.

Applications · Health

Yeongyeon Na, Minje Park, Yunwon Tae, Sunghoon Joo

Electrocardiograms (ECG) are widely employed as a diagnostic tool for monitoring electrical signals originating from a heart. Recent machine learning research efforts have focused on the application of screening various diseases using ECG signals. However, adapting to the application of screening disease is challenging in that labeled ECG data are limited. Achieving general representation through self-supervised learning (SSL) is a well-known approach to overcome the scarcity of labeled data; however, a naive application of SSL to ECG data, without considering the spatial-temporal relationships inherent in ECG signals, may yield suboptimal results. In this paper, we introduce ST-MEM (Spatio-Temporal Masked Electrocardiogram Modeling), designed to learn spatio-temporal features by reconstructing masked 12-lead ECG data. ST-MEM outperforms other SSL baseline methods in various experimental settings for arrhythmia classification tasks. Moreover, we demonstrate that ST-MEM is adaptable to various lead combinations. Through quantitative and qualitative analysis, we show a spatio-temporal relationship within ECG data. Our code is available at https://github.com/bakqui/ST-MEM.

Deep Learning · Theory

Linara Adilova, Maksym Andriushchenko, Michael Kamp, Asja Fischer, Martin Jaggi

Averaging neural network parameters is an intuitive method for fusing the knowledge of two independent models. It is most prominently used in federated learning. If models are averaged at the end of training, this can only lead to a good performing model if the loss surface of interest is very particular, i.e., the loss in the midpoint between the two models needs to be sufficiently low. This is impossible to guarantee for the non-convex losses of state-of-the-art networks. For averaging models trained on vastly different datasets, it was proposed to average only the parameters of particular layers or combinations of layers, resulting in better performing models. To get a better understanding of the effect of layer-wise averaging, we analyse the performance of the models that result from averaging single layers, or groups of layers. Based on our empirical and theoretical investigation, we introduce a novel notion of the layer-wise linear connectivity, and show that deep networks do not have layer-wise barriers between them.

Social Aspects · Trustworthy Machine Learning

Vaidehi Ramesh Patil, Peter Hase, Mohit Bansal

Pretrained language models sometimes possess knowledge that we do not wish them to, including memorized personal information and knowledge that could be used to harm people. They can also output toxic or harmful text. To mitigate these safety and informational issues, we propose an attack-and-defense framework for studying the task of deleting sensitive information directly from model weights. We study direct edits to model weights because (1) this approach should guarantee that particular deleted information is never extracted by future prompt attacks, and (2) it should protect against whitebox attacks, which is necessary for making claims about safety/privacy in a setting where publicly available model weights could be used to elicit sensitive information. Our threat model assumes that an attack succeeds if the answer to a sensitive question is located among a set of B generated candidates, based on scenarios where the information would be insecure if the answer is among B candidates. Experimentally, we show that even state-of-the-art model editing methods such as ROME struggle to truly delete factual information from models like GPT-J, as our whitebox and blackbox attacks can recover “deleted” information from an edited model 38% of the time. These attacks leverage two key observations: (1) that traces of deleted information can be found in intermediate model hidden states, and (2) that applying an editing method for one question may not delete information across rephrased versions of the question. Finally, we provide new defense methods that protect against some extraction attacks, but we do not find a single universally effective defense method. Our results suggest that truly deleting sensitive information is a tractable but difficult problem, since even relatively low attack success rates have potentially severe implications for the deployment of language models in a world where individuals enjoy ownership of their personal data, a right to privacy, and safety from harmful model outputs.

Miscellaneous Aspects of Machine Learning · Transfer, Multitask and Meta-learning

Zhan ZHUANG, Yu Zhang, Ying Wei

Domain shift degrades classification models on new data distributions. Conventional unsupervised domain adaptation (UDA) aims to learn features that bridge labeled source and unlabeled target domains. In contrast to feature learning, gradual domain adaptation (GDA) leverages extra continuous intermediate domains with pseudo-labels to boost the source classifier. However, real intermediate domains are sometimes unavailable or ineffective. In this paper, we propose $\textbf{G}$radual Domain Adaptation via $\textbf{G}$radient $\textbf{F}$low (GGF) to generate intermediate domains with preserving labels, thereby enabling us a fine-tuning method for GDA. We employ the Wasserstein gradient flow in Kullback–Leibler divergence to transport samples from the source to the target domain. To simulate the dynamics, we utilize the Langevin algorithm. Since the Langevin algorithm disregards label information and introduces diffusion noise, we introduce classifier-based and sample-based potentials to avoid label switching and dramatic deviations in the sampling process. For the proposed GGF model, we analyze its generalization bound. Experiments on several benchmark datasets demonstrate the superiority of the proposed GGF method compared to state-of-the-art baselines.

Miscellaneous Aspects of Machine Learning · Transfer, Multitask and Meta-learning

Zhipeng Zhou, Liu Liu, Peilin Zhao, Wei Gong

Deep long-tailed recognition (DTLR) has attracted much attention due to its close touch with realistic scenarios. Recent advances have focused on re-balancing across various aspects, e.g., sampling strategy, loss re-weighting, logit adjustment, and input/parameter perturbation, to name a few. However, few studies have considered dynamic re-balancing to address intrinsic optimization conflicts. In this paper, we first empirically argue that the optimizations of mainstream DLTR methods are still dominated by some categories (e.g., major) due to a fixed re-balancing strategy. Thus, they fail to deal with gradient conflicts among categories, which naturally deduces the motivation for reaching Pareto optimal solutions. Unfortunately, a naive integration of multi-objective optimization (MOO) with DLTR methods is not applicable due to the gap between multi-task learning (MTL) and DLTR, and can in turn lead to class-specific feature degradation. Thus, we provide effective alternatives by decoupling MOO-based MTL from the temporal rather than structure perspective, and enhancing it via optimizing variability collapse loss motivated by the derived MOO-based DLTR generalization bound. Moreover, we resort to anticipating worst-case optimization with theoretical insights to further ensure convergence. We build a Pareto deep long-tailed recognition method termed PLOT upon the proposed MOO framework. Extensive evaluations demonstrate that our method not only generally improves mainstream pipelines, but also achieves an augmented version to realize state-of-the-art performance across multiple benchmarks.

Deep Learning · Algorithms

Satoki Ishikawa, Ryo Karakida

Second-order optimization has been developed to accelerate the training of deep neural networks and it is being applied to increasingly larger-scale models. In this study, towards training on further larger scales, we identify a specific parameterization for second-order optimization that promotes feature learning in a stable manner even if the network width increases significantly. Inspired by a maximal update parametrization, we consider a one-step update of the gradient and reveal the appropriate scales of hyperparameters including random initialization, learning rates, and damping terms. Our approach covers two major second-order optimization algorithms, K-FAC and Shampoo, and we demonstrate that our parametrization achieves higher generalization performance in feature learning.In particular, it enables us to transfer the hyperparameters across models with different widths.

Optimization · Learning for Optimization

Xinyan Chen, Yang Li, Runzhong Wang, Junchi Yan

The Boolean satisfiability problem (SAT) stands as a canonical NP-complete task. In particular, the scarcity of real-world SAT instances and their usefulness for tuning SAT solvers underscore the necessity for effective and efficient ways of hard instance generation, whereas existing methods either struggle to maintain plausible hardness or suffer from limited applicability. Different from the typical construction-based methods, this paper introduces an adaptive and efficient graph interpolation approach that in place modifies the raw structure of graph-represented SAT instance by replacing it with a counterpart from another instance. Specifically, it involves a two-stage matching and mixing pipeline. The matching aims to find a correspondence map of literal nodes from two instance graphs via learned features from a matching network; while the mixing stage involves iteratively exchanging clause pairs with the highest correspondence scores until a specified replacement ratio is achieved. We further show that under our matching-mixing framework, moderate randomness can avoid hardness degradation of instances by introducing Gumbel noise. Experimental results show the superiority of our method with both resemblance in structure and hardness, and general applicability.

Deep Learning · Generative Models and Autoencoders

Xiangming Zhu, Huayu Deng, Haochen Yuan, Yunbo Wang, Xiaokang Yang

We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes. Our key insight is to use latent features drawn from a learnable prior distribution conditioned on the underlying particle states to capture the invisible and complex physical properties. To achieve this, we train a parametrized prior learner given visual observations to approximate the visual posterior of inverse graphics, and both the particle states and the visual posterior are obtained from a learned neural renderer. The converged prior learner is embedded in our probabilistic physics engine, allowing us to perform novel simulations on unseen geometries, boundaries, and dynamics without knowledge of the true physical parameters. We validate our model in three ways: (i) novel scene simulation with the learned visual-world physics, (ii) future prediction of the observed fluid dynamics, and (iii) supervised particle simulation. Our model demonstrates strong performance in all three tasks.

Miscellaneous Aspects of Machine Learning · Transfer, Multitask and Meta-learning

Wei-Hong Li, Steven McDonagh, Ales Leonardis, Hakan Bilen

Deep neural networks have become the standard solution for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks. However, the cross-task correlations that are learned in the unstructured feature space can be extremely noisy and susceptible to overfitting, consequently hurting performance. We propose to address this problem by introducing a structured 3D-aware regularizer which interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space and decodes them into their task output space through differentiable rendering. We show that the proposed method is architecture agnostic and can be plugged into various prior multi-task backbones to improve their performance; as we evidence using standard benchmarks NYUv2 and PASCAL-Context.

Deep Learning · Self-Supervised Learning

Sarthak Yadav, Sergios Theodoridis, Lars Kai Hansen, Zheng-Hua Tan

In this work, we propose a Multi-Window Masked Autoencoder (MW-MAE) fitted with a novel Multi-Window Multi-Head Attention (MW-MHA) module that facilitates the modelling of local-global interactions in every decoder transformer block through attention heads of several distinct local and global windows. Empirical results on ten downstream audio tasks show that MW-MAEs consistently outperform standard MAEs in overall performance and learn better general-purpose audio representations, along with demonstrating considerably better scaling characteristics. Investigating attention distances and entropies reveals that MW-MAE encoders learn heads with broader local and global attention. Analyzing attention head feature representations through Projection Weighted Canonical Correlation Analysis (PWCCA) shows that attention heads with the same window sizes across the decoder layers of the MW-MAE learn correlated feature representations which enables each block to independently capture local and global information, leading to a decoupled decoder feature hierarchy.

Applications · Computer Vision

Xinyu Huang, Youcai Zhang, Jinyu Ma, Weiwei Tian, Rui Feng, Yuejie Zhang, Yaqian Li, Yandong Guo, Lei Zhang

This paper presents Tag2Text, a vision language pre-training (VLP) framework, which introduces image tagging into vision-language models to guide the learning of visual-linguistic features. In contrast to prior works which utilize object tags either manually labeled or automatically detected with a limited detector, our approach utilizes tags parsed from its paired text to learn an image tagger and meanwhile provides guidance to vision-language models. Given that, Tag2Text can utilize large-scale annotation-free image tags in accordance with image-text pairs, and provides more diverse tag categories beyond objects. Strikingly, Tag2Text showcases the ability of a foundational image tagging model, with superior zero-shot performance even comparable to full supervision manner. Moreover, by leveraging tagging guidance, Tag2Text effectively enhances the performance of vision-language models on both generation-based and alignment-based tasks. Across a wide range of downstream benchmarks, Tag2Text achieves state-of-the-art results with similar model sizes and data scales, demonstrating the efficacy of the proposed tagging guidance.

Miscellaneous Aspects of Machine Learning · Transfer, Multitask and Meta-learning

Minh Hoang, Carl Kingsford

We tackle the problem of meta-learning across heterogenous tasks. This problem seeks to extract and generalize transferable meta-knowledge through streaming task sets from a multi-modal task distribution. The extracted meta-knowledge can be used to create predictors for new tasks using a small number of labeled samples. Most meta-learning methods assume a homogeneous task distribution, thus limiting their generalization capacity when handling multi-modal task distributions. Recent work has shown that the generalization of meta-learning depends on the similarity of tasks in the training distribution, and this has led to many clustering approaches that aim to detect homogeneous clusters of tasks. However, these methods suffer from a significant increase in parameter complexity. To overcome this weakness, we propose a new heterogeneous meta-learning strategy that efficiently captures the multi-modality of the task distribution via modulating the routing between convolution channels in the network, instead of directly modulating the network weights. This new mechanism can be cast as a permutation learning problem. We further introduce a novel neural permutation layer based on the classical Benes routing network, which has sub-quadratic parameter complexity in the total number of channels, as compared to the quadratic complexity of the state-of-the-art Gumbel-Sinkhorn layer. We demonstrate our approach on various multi-modal meta-learning benchmarks, showing that our framework outperforms previous methods in both generalization accuracy and convergence speed.

Applications · Language, Speech and Dialog

Duong Le, Yang Chen, Alan Ritter, Wei Xu

Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks that involve fine-grained predictions on words and phrases, the performance of zero-shot cross-lingual transfer learning lags far behind supervised fine-tuning methods. Therefore, it is common to exploit translation and label projection to further improve the performance by (1) translating training data that is available in a high-resource language (e.g., English) together with the gold labels into low-resource languages, and/or (2) translating test data in low-resource languages to a high-source language to run inference on, then projecting the predicted span-level labels back onto the original test data. However, state-of-the-art marker-based label projection methods suffer from translation quality degradation due to the extra label markers injected in the input to the translation model. In this work, we explore a new direction that leverages constrained decoding for label projection to overcome the aforementioned issues. Our new method not only can preserve the quality of translated texts but also has the versatility of being applicable to both translating training and translating test data strategies. This versatility is crucial as our experiments reveal that translating test data can lead to a considerable boost in performance compared to translating only training data. We evaluate on two cross-lingual transfer tasks, namely Named Entity Recognition and Event Argument Extraction, spanning 20 languages. The results demonstrate that our approach outperforms the state-of-the-art marker-based method by a large margin and also shows better performance than other label projection methods that rely on external word alignment.

Theory · Learning Theory

Noam Levi, Alon Beck, Yohai Bar-Sinai

Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surprisingly occur in linear networks performing linear tasks in a simple teacher-student setup. In this setting, the full training dynamics is derived in terms of the expected training and generalization data covariance matrix. We present exact predictions on how the grokking time depends on input and output dimensionality, train sample size, regularization, and network parameters initialization. The key findings are that late generalization increase may not imply a transition from "memorization" to "understanding", but can simply be an artifact of the accuracy measure. We provide empirical verification for these propositions, along with preliminary results indicating that some predictions also hold for deeper networks, with non-linear activations.

Applications · Health

Pengcheng Jiang, Cao Xiao, Adam Cross, Jimeng Sun

Clinical predictive models often rely on patients’ electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging. This is because personalized predictions require personalized knowledgegraphs (KGs), which are difficult to generate from patient EHR data. To address this, we propose GraphCare, an open-world framework that uses external KGs to improve EHR-based predictions. Our method extracts knowledge from large language models (LLMs) and external biomedical KGs to build patient-specific KGs, which are then used to train our proposed Bi-attention AugmenTed(BAT) graph neural network (GNN) for healthcare predictions. On two public datasets, MIMIC-III and MIMIC-IV, GraphCare surpasses baselines in four vital healthcare prediction tasks: mortality, readmission, length of stay (LOS), and drug recommendation. On MIMIC-III, it boosts AUROC by 17.6% and 6.6% for mortality and readmission, and F1-score by 7.9% and 10.8% for LOS and drug recommendation, respectively. Notably, GraphCare demonstrates a substantial edge in scenarios with limited data availability. Our findings highlight the potential of using external KGs in healthcare prediction tasks and demonstrate the promise of GraphCare in generating personalized KGs for promoting personalized medicine.

Deep Learning · Generative Models and Autoencoders

YEFEI HE, Jing Liu, Weijia Wu, Hong Zhou, Bohan Zhuang

Diffusion models have demonstrated remarkable capabilities in image synthesis and related generative tasks. Nevertheless, their practicality for low-latency real-world applications is constrained by substantial computational costs and latency issues. Quantization is a dominant way to compress and accelerate diffusion models, where post-training quantization (PTQ) and quantization-aware training (QAT) are two main approaches, each bearing its own properties. While PTQ exhibits efficiency in terms of both time and data usage, it may lead to diminished performance in low bit-width settings. On the other hand, QAT can help alleviate performance degradation but comes with substantial demands on computational and data resources. To capitalize on the advantages while avoiding their respective drawbacks, we introduce a data-free, quantization-aware and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency. Specifically, we propose a quantization-aware variant of the low-rank adapter (QALoRA) that can be merged with model weights and jointly quantized to low bit-width. The fine-tuning process distills the denoising capabilities of the full-precision model into its quantized counterpart, eliminating the requirement for training data. To further enhance performance, we introduce scale-aware optimization to address ineffective learning of QALoRA due to variations in weight quantization scales across different layers. We also employ temporal learned step-size quantization to handle notable variations in activation distributions across denoising steps. Extensive experimental results demonstrate that our method significantly outperforms previous PTQ-based diffusion models while maintaining similar time and data efficiency. Specifically, there is only a marginal $0.05$ sFID increase when quantizing both weights and activations of LDM-4 to 4-bit on ImageNet $256\times256$. Compared to QAT-based methods, our EfficientDM also boasts a $16.2\times$ faster quantization speed with comparable generation quality, rendering it a compelling choice for practical applications.