venue string | year string | paper_id string | title string | abstract string | decision string | label int64 | reviews list |
|---|---|---|---|---|---|---|---|
ICLR.cc | 2024 | cXs5md5wAq | Modelling Microbial Communities with Graph Neural Networks | Understanding the interactions and interplay of microorganisms is a great challenge with many applications in medical and environmental settings. In this work, we model bacterial communities directly from their genomes using graph neural networks (GNNs). GNNs leverage the inductive bias induced by the set nature of bac... | Reject | 0 | [
{
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.",
"questions": "What is the rationale for using a GNN on a very simp... |
ICLR.cc | 2024 | rhgIgTSSxW | TabR: Tabular Deep Learning Meets Nearest Neighbors | Deep learning (DL) models for tabular data problems (e.g. classification, regression) are currently receiving increasingly more attention from researchers.
However, despite the recent efforts, the non-DL algorithms based on gradient-boosted decision trees (GBDT) remain a strong go-to solution for these problems.
One of... | Accept (poster) | 1 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "1. Inference time and compexity -- are the stud... |
ICLR.cc | 2024 | kKRbAY4CXv | Neural Evolutionary Kernel Method: A Knowledge-Based Learning Architechture for Evolutionary PDEs | Numerical solution of partial differential equations (PDEs) plays a vital role in various fields of science and engineering. In recent years, deep neural networks (DNNs) have emerged as a powerful tool for solving PDEs. DNN-based methods exploit the approximation capabilities of neural networks to obtain solutions to P... | Reject | 0 | [
{
"confidence": "2: You are willing to defend your assessment, but it is quite likely that you did not understand the central parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "1. Can you provide more insight in... |
ICLR.cc | 2024 | ApjY32f3Xr | PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs | While significant progress has been made on Physics-Informed Neural Networks (PINNs), a comprehensive comparison of these methods across a wide range of Partial Differential Equations (PDEs) is still lacking. This study introduces PINNacle, a benchmarking tool designed to fill this gap. PINNacle provides a diverse data... | Reject | 0 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "Would it be possible to address the major issue... |
ICLR.cc | 2024 | eUgS9Ig8JG | SaNN: Simple Yet Powerful Simplicial-aware Neural Networks | Simplicial neural networks (SNNs) are deep models for higher-order graph representation learning. SNNs learn low-dimensional embeddings of simplices in a simplicial complex by aggregating features of their respective upper, lower, boundary, and coboundary adjacent simplices. The aggregation in SNNs is carried out durin... | Accept (spotlight) | 1 | [
{
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.",
"questions": "Could you respond to the above concern, and addition... |
ICLR.cc | 2024 | mnyXZBa5dP | Image Authenticity Detection using Eye Gazing Data: A Performance Comparison Beyond Human Capabilities via Attention Mechanism, ResNet, and Cascade Strategies | In the digital age, determining the authenticity of images has become
increasingly crucial. This study aims to explore the capability of machine
learning models in identifying manipulated images using eye movement data and
compares this with human judgment. We collected a series of both manipulated
and unaltered im... | null | 0 | [] |
ICLR.cc | 2024 | fMX07g3prp | FR-NAS: Forward-and-Reverse Graph Predictor for Efficient Neural Architecture Search | Neural Architecture Search (NAS) has risen to prominence as a pivotal tool for identifying optimal configurations for deep neural networks suited to particular tasks. However, the process of training and assessing numerous architectures introduces considerable computational overhead. One approach to mitigate this is th... | null | 0 | [
{
"confidence": "5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.",
"questions": "- Can the authors evaluate their method on the same framework as used in [4]? It would be great to see how FR-NAS performs under the... |
ICLR.cc | 2024 | qBL04XXex6 | Boosting of Thoughts: Trial-and-Error Problem Solving with Large Language Models | The reasoning performance of Large Language Models (LLMs) on a wide range of problems critically relies on chain-of-thought prompting, which involves providing a few chain of thought demonstrations as exemplars in prompts. Recent work, e.g., Tree of Thoughts, has pointed out the importance of exploration and self-evalu... | Accept (poster) | 1 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "- In Section 3.2, it is not quite clear how to ... |
ICLR.cc | 2024 | H9DYMIpz9c | Farzi Data: Autoregressive Data Distillation | We study data distillation for auto-regressive machine learning tasks, where the input and output have a strict left-to-right causal structure. More specifically, we propose Farzi, which summarizes an event sequence dataset into a small number of synthetic sequences — Farzi Data — which are optimized to maintain (if no... | Reject | 0 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "Listed in weakness section.",
"rating": "6:... |
ICLR.cc | 2024 | rp5vfyp5Np | BATTLE: Towards Behavior-oriented Adversarial Attacks against Deep Reinforcement Learning | Evaluating the performance of deep reinforcement learning (DRL) agents under adversarial attacks that aim to induce specific behaviors, i.e., behavior-oriented adversarial attacks, is crucial for understanding the robustness of DRL agents. Prior research primarily focuses on directing agents towards pre-determined stat... | Reject | 0 | [
{
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.",
"questions": "How are the behavior sequences generated for human p... |
ICLR.cc | 2024 | miGpIhquyB | Understanding Large Language Models Through the Lens of Dataset Generation | There has been increased interest in using Large Language Models (LLMs) for text dataset generation subject to a desired attribute, e.g., for use in downstream fine-tuning or training. These works generally focus on a single quality metric of the generated text, typically accuracy on a downstream task. However, this fa... | Reject | 0 | [
{
"confidence": "4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.",
"questions": "Have you tried variables besides temperature to test... |
ICLR.cc | 2024 | SMZnJtkNX5 | Temporal Parallelization for GPU Acceleration of Spiking Neural Networks | Inspired by neurobiological structures, Spiking Neural Networks (SNNs) are heralded as a significant advancement in deep learning, given their potential for superior computational efficiency. However, this potential often remains untapped on contemporary hardware platforms. Specifically, when deployed on standard GPUs,... | null | 0 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "* In Eq 3: $x_i^{(t, n)}$ should be $x_i^{(t, ... |
ICLR.cc | 2024 | 6AtXCnHCFy | FSN: Feature Shift Network for Load-Domain Domain Generalization | Conventional deep learning methods for fault detection often assume that the training and the testing sets share the same fault pattern spaces and domain spaces. However, some fault patterns are rare, and many real-world faults have not appeared in the training set. As a result, it’s hard for the trained model to achie... | null | 0 | [
{
"confidence": "3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.",
"questions": "- What does the \"Relation\" mean in Table 1?\n... |
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