GenAI4ELab/papercli-papers huggingface dataset:
Multi-instance Interactive Segmentation with Self-Supervised Transformer
Authors: Xavier Jiménez; Jaonary Rabarisoa; Valentin Belissen; Quoc Cuong PHAM
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The rise of Vision Transformers (ViT) combined with better self-supervised learning pre-tasks has taken representation learning to the next level, beating supervised results on ImageNet. In particular, self-attention mechanism of ViT allows to easily visualize semantic information learned by the network. Following revealing of attention maps of DINO, many tried to leverage its representations for unsupervised segmentation. Despite very promising results for basic images with a single clear object in a simple background, representation of ViT are not able to segment images, with several classes and object instance, in an unsupervised fashion yet. In this paper, we propose SALT: Semi-supervised Segmentation with Self-supervised Attention Layers in Transformers, an interactive algorithm for multi-class/multi-instance segmentation. We follow previous works path and take it a step further by discriminating between different objects, using sparse human help to select said objects. We show that remarkable results are achieved with very sparse labels. Different pre-tasks are compared, and we show that self-supervised ones are more robust for panoptic segmentation, and overall achieve very similar performance. Evaluation is carried out on Pascal VOC 2007 and COCO-panoptic. Performance is evaluated for extreme conditions such as very noisy, and sparse interactions going to as little as one interaction per class.
MolJET: Multimodal Joint Embedding Transformer for Conditional de novo Molecular Design and Multi-Property Optimization
Authors: Orion Walker Dollar; Sameera Horawalavithana; Scott Vasquez; W. James Pfaendtner; Svitlana Volkova
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Multi-property constrained optimization of molecules using generative de novo design models is vital for the successful application of Artificial Intelligence (AI) towards materials and drug discovery. Yet there remains a gap between the reported performance of such models in the literature and their practical utility in real world design scenarios. Furthermore, existing models are largely inaccessible to chemists without an extensive background in computer science. To address these challenges, we propose a generative foundation model, the Multimodal Joint Embedding Transformer (MolJET), which performs conditional generation of desired molecular distributions based on human-interpretable chemistry prompts in a zero-shot manner. We assess MolJET on the standard benchmarks available in the GuacaMol and MIMOSA evaluation frameworks. These include structure-based sampling tasks as well as a range of multi-property optimization tasks that probe a models ability to design drug-like molecules given realistic property constraints. We demonstrate that with self-supervised pretraining, MolJET outperforms 80% of task-optimized models while using zero-shot inferences and beats all baselines after minimal supervision. Moreover, the performance of MolJET on text-only conditioning tasks improves with the inclusion of property modalities during training, highlighting the importance of a multimodal approach to molecular design. MolJET is the first example of text-based de novo molecular design using large-scale multimodal foundation models and should serve as a building block towards further improvements to accessible AI for chemists.
An Efficient Mean-field Approach to High-Order Markov Logic
Authors: Weidi Xu; Jianshan He; Jingwei Wang; Hongting Zhou; Xiaopei Wan; Taifeng Wang; Ruopeng Li; Wei Chu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Markov logic networks (MLNs) are powerful models for symbolic reasoning, which combine probabilistic modeling with relational logic. Inference algorithms for MLNs often perform at the level of propositional logic or require building a first-order probabilistic graph, and the computational efficiency remains a challenge. The mean-field algorithm generalizes message passing for approximate inference in many intractable probabilistic graphical models, but in MLNs it still suffers from the high-order dependencies among the massive groundings, resulting in time complexity exponential in both the length and the arity of logic rules. We propose a novel method, LogicMP, to simplify the logic message passing especially. In most practical cases, it can reduce the complexity significantly to polynomial for the formulae in conjunctive normal form (CNF). We exploit the property of CNF logic rules to sidestep the expectation computation of high-order dependency, and then formulate the logic message passing by Einstein summation to facilitate parallel computation, which can be optimized by sequentially contracting the rule arguments. With LogicMP, we achieve evident improvements on several reasoning benchmark datasets in both performance and efficiency over competitor methods. Specifically, the AUC-PR of the UW-CSE and Cora datasets is improved by more than 11\% absolutely and the speed is about ten times faster.
Does progress on ImageNet transfer to real world datasets?
Authors: Alex Fang; Simon Kornblith; Ludwig Schmidt
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Does progress on ImageNet transfer to real world datasets? We investigate this question by evaluating ImageNet pre-trained models with varying accuracy (57% - 83%) on six practical image classification datasets. In particular, we study datasets collected with the goal of solving real world tasks (e.g., classifying images from camera traps or satellites), as opposed to web-scraped benchmarks collected for comparing models. On multiple datasets, models with higher ImageNet accuracy do not consistently yield performance improvements. For certain tasks, interventions such as data augmentation improve performance even when architectures do not. We hope that future benchmarks will include more diverse datasets to encourage a more comprehensive approach to improving learning algorithms.
AdaStride: Using Adaptive Strides in Sequential Data for Effective Downsampling
Authors: Yoonhyung Lee; Kyomin Jung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: The downsampling layer has been one of the most commonly used deep learning (DL) components in sequential data processing due to its several advantages. First, it improves the generalization performance of networks by acting as an information bottleneck, where it extracts task-relevant features and discards others. Second, it reduces data resolution allowing CNN layers to have larger receptive fields with smaller kernel sizes. Third, the reduced data resolution facilitates the use of Transformer networks in case of high-resolution data. Accordingly, there have been many studies on downsampling methods, but they have a limitation in that they apply the same downsampling ratio across a data instance. Using the same downsampling ratio uniformly for an entire data instance does not reflect the fact that the task-relevant information is not uniformly distributed in real data. In this paper, we introduce AdaStride, a downsampling method that can apply adaptively varying downsampling ratios across a sequential data instance given an overall downsampling ratio. Specifically, AdaStride learns to deploy adaptive strides in a sequential data instance. Therefore, it can preserve more information from task-relevant parts of a data instance by using smaller strides for those parts and larger strides for less relevant parts. To achieve this, we propose a novel training method called vector positioning that rearranges each time step of an input on a one-dimensional line segment without reordering, which is used to build an alignment matrix for the downsampling. In experiments conducted on three different tasks of audio classification, automatic speech recognition, and discrete representation learning, AdaStride outperforms other widely used standard downsampling methods showing its generality and effectiveness. In addition, we analyze how our AdaStride learns the effective adaptive strides to improve its performance in the tasks.
Slimmable Networks for Contrastive Self-supervised Learning
Authors: Shuai Zhao; Xiaohan Wang; Linchao Zhu; Yi Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Self-supervised learning makes great progress in large model pre-training but suffers in training small models. Previous solutions to this problem mainly rely on knowledge distillation and indeed have a two-stage learning procedure: first train a large teacher model, then distill it to improve the generalization ability of small ones. In this work, we present a new one-stage solution to obtain pre-trained small models without extra teachers: slimmable networks for contrastive self-supervised learning (SlimCLR). A slimmable network contains a full network and several weight-sharing sub-networks. We can pre-train for only one time and obtain various networks including small ones with low computation costs. However, in self-supervised cases, the interference between weight-sharing networks leads to severe performance degradation. One evidence of the interference is gradient imbalance: a small proportion of parameters produces dominant gradients during backpropagation, and the main parameters may not be fully optimized. The divergence in gradient directions of various networks may also cause interference between networks. To overcome these problems, we make the main parameters produce dominant gradients and provide consistent guidance for sub-networks via three techniques: slow start training of sub-networks, online distillation, and loss re-weighting according to model sizes. Besides, a switchable linear probe layer is applied during linear evaluation to avoid the interference of weight-sharing linear layers. We instantiate SlimCLR with typical contrastive learning frameworks and achieve better performance than previous arts with fewer parameters and FLOPs.
Current Anomaly Detectors are Anomalous: On Semantic Treatment of OOD Inputs
Authors: Ramneet Kaur; Xiayan Ji; Souradeep Dutta; Yahan Yang; Michele Caprio; Elena Bernardis; Oleg Sokolsky; Insup Lee
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Machine learning models have achieved impressive performance across different modalities. It is well known that these models are prone to making mistakes on out-of-distribution inputs. OOD detection has, therefore, gained a lot of attention recently. We observe that most existing detectors use the distribution estimated by the training dataset for OOD detection. This can be a serious impediment since faulty OOD detectors can potentially restrict utility of the model. Such detectors, tied to the bias in data collection process,  can be impermeable to inputs lying outside the training distribution but with the same semantic information (e.g., class labels) as the training data. We argue that in-distribution should not be tied to just the training distribution but to the distribution of the semantic information contained in the training data. To support our argument, we perform OOD detection on semantic information extracted from the training data of MNIST and COCO datasets, and show that it not only reduces false alarms but also significantly improves detection of OOD inputs with spurious features from training data.
$\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks
Authors: Zeyu Zhu; Fanrong Li; Zitao Mo; Qinghao Hu; Gang Li; Zejian Liu; Xiaoyao Liang; Jian Cheng
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: As graph data size increases, the vast latency and memory consumption during inference pose a significant challenge to the real-world deployment of Graph Neural Networks (GNNs). While quantization is a powerful approach to reducing GNNs complexity, most previous works on GNNs quantization fail to exploit the unique characteristics of GNNs, suffering from severe accuracy degradation. Through an in-depth analysis of the topology of GNNs, we observe that the topology of the graph leads to significant differences between nodes, and most of the nodes in a graph appear to have a small aggregation value. Motivated by this, in this paper, we propose the Aggregation-Aware mixed-precision Quantization ($\rm A^2Q$) for GNNs, where an appropriate bitwidth is automatically learned and assigned to each node in the graph. To mitigate the vanishing gradient problem caused by sparse connections between nodes, we propose a Local Gradient method to serve the quantization error of the node features as the supervision during training. We also develop a Nearest Neighbor Strategy to deal with the generalization on unseen graphs. Extensive experiments on eight public node-level and graph-level datasets demonstrate the generality and robustness of our proposed method. Compared to the FP32 models, our method can achieve up to $18.8\times$ (i.e., 1.70bits) compression ratio with negligible accuracy degradation. Moreover, compared to the state-of-the-art quantization method, our method can achieve up to $11.4\%$ and $9.5\%$ accuracy improvements on the node-level and graph-level tasks, respectively, and up to $2\times$ speedup on a dedicated hardware accelerator.
Bootstrap Motion Forecasting With Self-Consistent Constraints
Authors: Maosheng Ye; jiamiao xu; xunnong xu; Tengfei Wang; Tongyi Cao; Qifeng Chen
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We present a novel framework to bootstrap Motion forecasting with self-consistent constraints (MISC). The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. A key design of MISC is the proposed Dual Consistency Constraints that regularize the predicted trajectories under spatial and temporal perturbation during training. Also, to model the multi-modality in motion forecasting,  we design a novel self-ensembling scheme to obtain accurate teacher targets to enforce the self-constraints with multi-modality supervision. With explicit constraints from multiple teacher targets, we observe a clear improvement in the prediction performance. Extensive experiments on the Argoverse motion forecasting benchmark show that MISC significantly outperforms the state-of-the-art methods. As the proposed strategies are general and can be easily incorporated into other motion forecasting approaches, we also demonstrate that our proposed scheme consistently improves the prediction performance of several existing methods.
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Authors: Michał Zawalski; Michał Tyrolski; Konrad Czechowski; Tomasz Odrzygóźdź; Damian Stachura; Piotr Piękos; Yuhuai Wu; Łukasz Kuciński; Piotr Miłoś
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Complex reasoning problems contain states that vary in the computational cost required to determine the right action plan. To take advantage of this property, we propose Adaptive Subgoal Search (AdaSubS), a search method that adaptively adjusts the planning horizon. To this end, AdaSubS generates diverse sets of subgoals at different distances. A verification mechanism is employed to filter out unreachable subgoals swiftly, making it possible to focus on feasible further subgoals. In this way, AdaSubS benefits from the efficiency of planning with longer-term subgoals and the fine control with shorter-term ones, and thus scales well to difficult planning problems. We show that AdaSubS significantly surpasses hierarchical planning algorithms on three complex reasoning tasks: Sokoban, the Rubik’s Cube, and the inequality-proving benchmark INT.
Learning Structured Representations by Embedding Class Hierarchy
Authors: Siqi Zeng; Remi Tachet des Combes; Han Zhao
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Existing models for learning representations in supervised classification problems are permutation invariant with respect to class labels. However, structured knowledge about the classes, such as hierarchical label structures, widely exists in many real-world datasets, e.g., the ImageNet and CIFAR benchmarks. How to learn representations that can preserve such structures among the classes remains an open problem. To approach this problem, given a tree of class hierarchy, we first define a tree metric between any pair of nodes in the tree to be the length of the shortest path connecting them. We then provide a method to learn the hierarchical relationship of class labels by approximately embedding the tree metric in the Euclidean space of features. More concretely, during supervised training, we propose to use the Cophenetic Correlation Coefficient (CPCC) as a regularizer for the cross-entropy loss to correlate the tree metric of classes and the Euclidean distance in the class-conditioned representations. Our proposed regularizer is computationally lightweight and easy to implement. Empirically, we demonstrate that this approach can help to learn more interpretable representations due to the preservation of the tree metric, and leads to better in-distribution generalization as well as under sub-population shifts over six real-world datasets.
Domain Generalisation via Domain Adaptation: An Adversarial Fourier Amplitude Approach
Authors: Minyoung Kim; Da Li; Timothy Hospedales
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: We tackle the domain generalisation (DG) problem by posing it as a domain adaptation (DA) task where we adversarially synthesise the worst-case `target' domain and adapt a model to that worst-case domain, thereby improving the model’s robustness. To synthesise data that is challenging yet semantics-preserving, we generate Fourier amplitude images and combine them with source domain phase images, exploiting the widely believed conjecture from signal processing that amplitude spectra mainly determines image style, while phase data mainly captures image semantics. To synthesise a worst-case domain for adaptation, we train the classifier and the amplitude generator adversarially. Specifically, we exploit the maximum classifier discrepancy (MCD) principle from DA that relates the target domain performance to the discrepancy of classifiers in the model hypothesis space. By Bayesian hypothesis modeling, we express the model hypothesis space effectively as a posterior distribution over classifiers given the source domains, making adversarial MCD minimisation feasible. On the DomainBed benchmark including the large-scale DomainNet dataset, the proposed approach yields significantly improved domain generalisation performance over the state-of-the-art.
Cross-Protein Wasserstein Transformer for Protein-Protein Interactions
Authors: Gongping Xu; Tong Zhang; Wenting Zhao; Zhen Cui; Jian Yang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Previous studies reveal intimate relationships between the structure and function of proteins. Motivated by this, for protein-protein interactions (PPIs), we hypothesize that cross-protein structural correspondence, including both global correlation and local co-occurrence, poses a great influence. Accordingly, a novel deep learning framework named Cross-Protein Wasserstein Transformer (CPWT) is proposed to predict PPI sites through fine-grained cross-graph structural modeling. Considering the irregular architecture of acid sequences, for a pair of proteins, graphs are constructed to describe them. Then, a core Cross-Graph Transformer (CGT) module of two branches (e.g. ligand and receptor branches) is proposed for cross-protein structural modeling. Specifically, in this module, Wasserstein affinity across graphs is calculated through cross-graph query (i.e. ligand (query) - receptor (key) or the converse), based on which the multi-head attention is derived to adaptively mine fine-grained cues of PPI sites. By stacking CGT modules, the two branches in CGT are co-evolved in a deep architecture during forward inference, hence being powerful and advantageous in cross-protein structural representation and fine-grained learning. We verify the effectiveness of our CPWT framework by conducting comprehensive experiments on multiple PPI datasets, and further visualize the learned fine-grained saliencies for intuitive understanding.
Towards Robustness Certification Against Universal Perturbations
Authors: Yi Zeng; Zhouxing Shi; Ming Jin; Feiyang Kang; Lingjuan Lyu; Cho-Jui Hsieh; Ruoxi Jia
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this paper, we investigate the problem of certifying neural network robustness against universal perturbations (UPs), which have been widely used in universal adversarial attacks and backdoor attacks. Existing robustness certification methods aim to provide robustness guarantees for each sample with respect to the worst-case perturbations given a neural network. However, those sample-wise bounds will be loose when considering the UP threat model as they overlook the important constraint that the perturbation should be shared across all samples. We propose a method based on a combination of linear relaxation-based perturbation analysis and Mixed Integer Linear Programming to establish the first robust certification method for UP. In addition, we develop a theoretical framework for computing error bounds on the entire population using the certification results from a randomly sampled batch. Aside from an extensive evaluation of the proposed certification, we further show how the certification facilitates efficient comparison of robustness among different models or efficacy among different universal adversarial attack defenses and enables accurate detection of backdoor target classes.
Contrastive Unsupervised Learning of World Model with Invariant Causal Features
Authors: Rudra P. K. Poudel; Harit Pandya; Roberto Cipolla
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper we present a world model, which learns the causal features using invariance principle. We use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation. Since the world model based reinforcement learning methods optimize representation learning and policy of the agent independently, contrastive loss collapses due to lack of supervisory signal to the representation learning module. We propose depth reconstruction as an auxiliary task to explicitly enforce the invariance and data augmentation as style intervention on the RGB space to mitigate this issue. Our design help us to leverage state-of-the-art unsupervised representation learning method to learn the world model with invariant causal features, which outperforms current state-of-the-art model-based as well as model-free reinforcement learning methods on out-of-distribution point navigation tasks on Gibson and iGibson dataset at 100k and 500k interaction step benchmarks. Further experiments on DeepMind control suite even without depth reconstruction, our proposed model performs on par with the state-of-the-art counterpart models.