GenAI4ELab/papercli-papers huggingface dataset:
De Novo Molecular Generation via Connection-aware Motif Mining
Authors: Zijie Geng; Shufang Xie; Yingce Xia; Lijun Wu; Tao Qin; Jie Wang; Yongdong Zhang; Feng Wu; Tie-Yan Liu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibility in generating novel molecules based on existing molecule fragments. However, the motif vocabulary, i.e., the collection of frequent fragments, is usually built upon heuristic rules, which brings difficulties to capturing common substructures from large amounts of molecules. In this work, we propose MiCaM to generate molecules based on mined connection-aware motifs. Specifically, it leverages a data-driven algorithm to automatically discover motifs from a molecule library by iteratively merging subgraphs based on their frequency. The obtained motif vocabulary consists of not only molecular motifs (i.e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other. Based on the mined connection-aware motifs, MiCaM builds a connection-aware generator, which simultaneously picks up motifs and determines how they are connected. We test our method on distribution-learning benchmarks (i.e., generating novel molecules to resemble the distribution of a given training set) and goal-directed benchmarks (i.e., generating molecules with target properties), and achieve significant improvements over previous fragment-based baselines. Furthermore, we demonstrate that our method can effectively mine domain-specific motifs for different tasks.
Meta Temporal Point Processes
Authors: Wonho Bae; Mohamed Osama Ahmed; Frederick Tung; Gabriel L. Oliveira
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A temporal point process (TPP) is a stochastic process where its realization is a sequence of discrete events in time. Recent work in TPPs model the process using a neural network in a supervised learning framework, where a training set is a collection of all the sequences. In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs). We introduce context sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce local history matching to help learn more informative features. We demonstrate the potential of the proposed method on popular public benchmark datasets and tasks, and compare with state-of-the-art TPP methods.
SEQuence-rPPG: A Fast BVP Signal Extraction Method From Frame Sequences
Authors: Kegang Wang; Yantao Wei; Mingwen Tong; Jie Gao; ZhongJin Zhao; YuJian Ma; Yi Tian
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Non-contact heart rate estimation has essential implications for the development of affective computing and telemedicine. However, existing deep learning-based methods often endeavor to achieve real-time measurements, so a simple, fast, pre-processing-free approach is needed. Our work consists of two main parts. Firstly, we proposed SEQ-rPPG, which first transforms the RGB frame sequence into the original BVP signal sequence by learning-based linear mapping and then outputs the final BVP signal using 1DCNN-based spectral transform, and time-domain filtering. Secondly, to address the shortcomings of the existing dataset in training the model, a new large-scale dataset was collected for training and testing. Our approach achieved competitive results on the collected large dataset(the best) and public dataset UBFC-rPPG(0.81 MAE with 30s time window, test only). It requires no complex pre-processing, has the fastest speed, can run in real-time on mobile ARM CPUs, and can achieve real-time beat-to-beat performance on desktop CPUs. Benefiting from the high-quality training set, other deep learning-based models reduced errors by at least 53$\%$. We compared the methods with and without the spectral transformation, and the results show that the processing in the time domain is effective.
Fake It Until You Make It : Towards Accurate Near-Distribution Novelty Detection
Authors: Hossein Mirzaei; Mohammadreza Salehi; Sajjad Shahabi; Efstratios Gavves; Cees G. M. Snoek; Mohammad Sabokrou; Mohammad Hossein Rohban
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We aim for image-based novelty detection. Despite considerable progress, existing models either fail or face dramatic drop under the so-called ``near-distribution" setup, where the differences between normal and anomalous samples are subtle. We first demonstrate existing methods could experience up to 20\% decrease in their AUCs in the near-distribution setting. Next, we propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our model is then fine-tuned to distinguish such data from the normal samples. We make quantitative as well as qualitative evaluation of this strategy, and compare the results with a variety of GAN-based models.  Effectiveness of our method for both near-distribution and standard novelty detection is assessed through extensive experiments on datasets in diverse applications such as medical images, object classification, and quality control. This reveals that our method significantly improves upon existing models, and consistently decreases the gap between the near-distribution and standard novelty detection AUCs by a considerable amount.
Ensuring DNN Solution Feasibility for Optimization Problems with Linear Constraints
Authors: Tianyu Zhao; Xiang Pan; Minghua Chen; Steven Low
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: We propose preventive learning as the first framework to guarantee Deep Neural Network (DNN) solution feasibility for optimization problems with linear constraints without post-processing, upon satisfying a mild condition on constraint calibration. Without loss of generality, we focus on problems with only inequality constraints. We systematically calibrate the inequality constraints used in training, thereby anticipating DNN prediction errors and ensuring the obtained solutions remain feasible. We characterize the calibration rate and a critical DNN size, based on which we can directly construct a DNN with provable solution feasibility guarantee. We further propose an Adversarial-Sample Aware training algorithm to improve its optimality performance. We apply the framework to develop DeepOPF+ for solving essential DC optimal power flow problems in grid operation. Simulation results over IEEE test cases show that it outperforms existing strong DNN baselines in ensuring 100\% feasibility and attaining consistent optimality loss (<0.19%) and speedup (up to x228) in both light-load and heavy-load regimes, as compared to a state-of-the-art solver. We also apply our framework to a non-convex problem and show its performance advantage over existing schemes.
CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving
Authors: Runjian Chen; Yao Mu; Runsen Xu; Wenqi Shao; Chenhan Jiang; Hang Xu; Yu Qiao; Zhenguo Li; Ping Luo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Unsupervised contrastive learning for indoor-scene point clouds has achieved great successes. However, unsupervised representation learning on outdoor-scene point clouds remains challenging because previous methods need to reconstruct the whole scene and capture partial views for the contrastive objective. This is infeasible in outdoor scenes with moving objects, obstacles, and sensors. In this paper, we propose CO3, namely {Co}operative {Co}ntrastive Learning and {Co}ntextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner. CO3 has several merits compared to existing methods. (1) It utilizes LiDAR point clouds from vehicle-side and infrastructure-side to build views that differ enough but meanwhile maintain common semantic information for contrastive learning, which are more appropriate than views built by previous methods. (2) Alongside the contrastive objective, we propose contextual shape prediction to bring more task-relevant information for unsupervised 3D point cloud representation learning and we also provide a theoretical analysis for this pre-training goal. (3) As compared to previous methods, representation learned by CO3 is able to be transferred to different outdoor scene dataset collected by different type of LiDAR sensors. (4) CO3 improves current state-of-the-art methods on Once, KITTI and NuScenes datasets by up to 2.58 mAP in 3D object detection task and 3.54 mIoU in LiDAR semantic segmentation task. Codes and models will be released.
DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS
Authors: Heng Li; Xiaodong Gu; Weihao Yuan; luwei yang; Zilong Dong; Ping Tan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods.The code is at poptree.github.io/DIM-SLAM/.
Achieving Near-Optimal Individual Regret & Low Communications in Multi-Agent Bandits
Authors: Xuchuang Wang; Lin Yang; Yu-Zhen Janice Chen; Xutong Liu; Mohammad Hajiesmaili; Don Towsley; John C.S. Lui
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Cooperative multi-agent multi-armed bandits (CM2AB) study how distributed agents cooperatively play the same multi-armed bandit game. Most existing CM2AB works focused on maximizing the group performance of all agents---the accumulation of all agents' individual performance (i.e., individual reward). However, in many applications, the performance of the system is more sensitive to the ``bad'' agent---the agent with the worst individual performance. For example, in a drone swarm, a ``bad'' agent may crash into other drones and severely degrade the system performance. In that case, the key of the learning algorithm design is to coordinate computational and communicational resources among agents so to optimize the individual learning performance of the ``bad'' agent. In CM2AB, maximizing the group performance is equivalent to minimizing the group regret of all agents, and minimizing the individual performance can be measured by minimizing the maximum (worst) individual regret among agents. Minimizing the maximum individual regret was largely ignored in prior literature, and currently, there is little work on how to minimize this objective with a low communication overhead. In this paper, we propose a near-optimal algorithm on both individual and group regrets, in addition,  we also propose a novel communication module in the algorithm, which only needs \(O(\log (\log T))\) communication times where \(T\) is the number of decision rounds. We also conduct simulations to illustrate the advantage of our algorithm by comparing it to other known baselines.
Contrastive Audio-Visual Masked Autoencoder
Authors: Yuan Gong; Andrew Rouditchenko; Alexander H. Liu; David Harwath; Leonid Karlinsky; Hilde Kuehne; James R. Glass
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks, to learn a joint and coordinated audio-visual representation.
Our experiments show that the contrastive audio-visual correspondence learning objective not only enables the model to perform audio-visual retrieval tasks, but also helps the model learn a better joint representation. As a result, our fully self-supervised pretrained CAV-MAE achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the previous best supervised pretrained model on AudioSet in the audio-visual event classification task. Code and pretrained models are at https://github.com/yuangongnd/cav-mae.
Hidden Markov Mixture of Gaussian Process Functional Regression: Utilizing Multi-Scale Structure for Time-Series Forecasting
Authors: Tao Li; Jinwen Ma
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The mixture of Gaussian process functional regressions (GPFRs) assumes that there are a batch of time-series or sample curves which are  generated by independent random processes with different temporal structures. However, in the real situations, these structures are actually transferred in a random manner from a long time scale. Therefore, the assumption of independent curves is not true in practice. In order to get rid of this limitation, we propose the hidden Markov based GPFR mixture model (HM-GPFR) by describing these curves with both fine and coarse level temporal structures. Specifically, the temporal structure is described by the Gaussian process model at the fine level and hidden Markov process at the coarse level. The whole model can be regarded as a random process with state switching dynamics. To further enhance the robustness of the model, we also give a priori to the model parameters and develop Bayesian hidden Markov based GPFR mixture model (BHM-GPFR). Experimental results demonstrate that the proposed methods have both high prediction accuracy and good interpretability.
Incomplete to complete multiphysics forecasting - a hybrid approach for learning unknown phenomena
Authors: Nilam Nandkishor Tathawadekar; Nguyen Anh Khoa Doan; Camilo Fernando Silva; Nils Thuerey
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Modeling complex dynamical systems where only partial knowledge of their physical mechanisms is available is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution of the system dynamics over a sufficiently long time and in a physically consistent manner. Therefore, we propose a hybrid approach that uses a neural network model in combination with an incomplete PDE solver that provides known but incomplete physical information. In this study, we demonstrate that the results obtained from the incomplete PDEs can be efficiently corrected at every time step by the proposed hybrid neural network – PDE solver model, so that the effect of the unknown physics present in the system is correctly accounted for. For validation purposes, the obtained simulations of the hybrid model are successfully compared against results coming from the complete set of PDEs describing the full physics of the considered system. We demonstrate the validity of the proposed approach on a reactive flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Experiments are made on planar and Bunsen-type flames at various operating conditions. The hybrid neural network - PDE approach correctly models the flame evolution of the cases under study for significantly long time windows, yields improved generalization, and allows for larger simulation time steps.
Data Leakage in Tabular Federated Learning
Authors: Mark Vero; Mislav Balunovic; Dimitar Iliev Dimitrov; Martin Vechev
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: While federated learning (FL) promises to preserve privacy in distributed training of deep learning models, recent work in the image and NLP domains showed that training updates leak private data of participating clients. At the same time, most high-stakes applications of FL (e.g., legal and financial) use tabular data. Compared to the NLP and image domains, reconstruction of tabular data poses several unique challenges: (i) categorical features introduce a significantly more difficult mixed discrete-continuous optimization problem, (ii) the mix of categorical and continuous features causes high variance in the final reconstructions, and (iii) structured data makes it difficult for the adversary to judge reconstruction quality. In this work, we tackle these challenges and propose the first comprehensive reconstruction attack on tabular data, called TabLeak. TabLeak is based on three key ingredients: (i) a softmax structural prior, implicitly converting the mixed discrete-continuous optimization problem into an easier fully continuous one, (ii) a way to reduce the variance of our reconstructions through a pooled ensembling scheme exploiting the structure of tabular data, and (iii) an entropy measure which  can successfully assess reconstruction quality. Our experimental evaluation demonstrates the effectiveness of TabLeak, reaching a state-of-the-art on four popular tabular datasets. For instance, on the Adult dataset, we improve attack accuracy by 10% compared to the baseline on the practically relevant batch size of 32 and further obtain non-trivial reconstructions for batch sizes as large as 128. Our findings are important as they show that performing FL on tabular data, which often poses high privacy risks, is highly vulnerable.
Noise$^+$2Noise: Co-taught De-noising Autoencoders for Time-Series Data
Authors: Harry Rubin-Falcone; Joyce Lee; Jenna Wiens
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We consider the task of learning to recover clean signals given only access to noisy data. Recent work in computer vision has addressed this problem in the context of images using denoising autoencoders (DAEs).  However, to date DAEs for learning from noisy data have not been explored in the context of time-series data.  DAEs for denoising images often rely on assumptions unlikely to hold in the context of time series, \textit{e.g.}, multiple noisy samples of the same example. Here, we adapt DAEs to cleaning time-series data with noisy samples only. To recover the clean target signal when only given access to noisy target data,  we leverage a noise-free auxiliary time-series signal that is related to the target signal. In addition to leveraging the relationship between the target signal and auxiliary signal, we iteratively filter and learn from clean samples using an approach based on co-teaching. Applied to the task of recovering carbohydrate values for blood glucose management, our approach reduces noise (MSE) in patient-reported carbohydrates from 72$g^2$ (95\% CI: 54,93) to 18$g^2$ (13,25), outperforming the best baseline (MSE = 33$g^2$ (27,43)). We demonstrate strong time-series denoising performance, extending the applicability of DAEs to a previously under-explored setting.
Uncovering Directions of Instability via Quadratic Approximation of Deep Neural Loss in Reinforcement Learning
Authors: Ezgi Korkmaz; Jonah Brown-Cohen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Learning in MDPs with highly complex state representations is currently possible due to multiple advancements in reinforcement learning algorithm design. However, this incline in complexity, and furthermore the increase in the dimensions of the observation came at the cost of non-robustness that can be taken advantage of (i.e. moving along worst-case directions in the observation space). To solve this policy instability problem we propose a novel method to ascertain the presence of these non-robust directions via quadratic approximation of the deep neural policy loss. Our method provides a theoretical basis for the fundamental cut-off between stable observations and non-robust observations.  Furthermore, our technique is computationally efficient, and does not depend on the methods used to produce the worst-case directions. We conduct extensive experiments in the Arcade Learning Environment with several different non-robust alteration techniques. Most significantly, we demonstrate the effectiveness of our approach even in the setting where alterations are explicitly optimized to circumvent our proposed method.
Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings
Authors: Frederike Lübeck; Charlotte Bunne; Gabriele Gut; Jacobo Sarabia del Castillo; Lucas Pelkmans; David Alvarez-Melis
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Comparing unpaired samples of a distribution or population taken at different points in time is a fundamental task in many application domains where measuring populations is destructive and cannot be done repeatedly on the same sample, such as in single-cell biology. Optimal transport (OT) can solve this challenge by learning an optimal coupling of samples across distributions from unpaired data. However, the usual formulation of OT assumes conservation of mass, which is violated in unbalanced scenarios in which the population size changes (e.g., cell proliferation or death) between measurements. In this work, we introduce NubOT, a neural unbalanced OT formulation that relies on the formalism of semi-couplings to account for creation and destruction of mass. To estimate such semi-couplings and generalize out-of-sample, we derive an efficient parameterization based on neural optimal transport maps and propose a novel algorithmic scheme through a cycle-consistent training procedure. We apply our method to the challenging task of forecasting heterogeneous responses of multiple cancer cell lines to various drugs, where we observe that by accurately modeling cell proliferation and death, our method yields notable improvements over previous neural optimal transport methods.
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles
Authors: Biswadeep Chakraborty; Saibal Mukhopadhyay
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: This paper shows that the heterogeneity in neuronal and synaptic dynamics reduces the spiking activity of a Recurrent Spiking Neural Network (RSNN) while improving prediction performance, enabling spike-efficient (unsupervised) learning.
We analytically show that the diversity in neurons' integration/relaxation dynamics improves an RSNN's ability to learn more distinct input patterns (higher memory capacity), leading to improved classification and prediction performance. We further prove that heterogeneous Spike-Timing-Dependent-Plasticity (STDP) dynamics of synapses reduce spiking activity but preserve memory capacity. The analytical results motivate Heterogeneous RSNN design using Bayesian optimization to determine heterogeneity in neurons and synapses to improve $\mathcal{E}$, defined as the ratio of spiking activity and memory capacity. The empirical results on time series classification and prediction tasks show that optimized HRSNN increases performance and reduces spiking activity compared to a homogeneous RSNN.