abstracts sequencelengths 2 2 | id_1 int64 0 1.57k | id_2 int64 0 1.57k | pair_id stringlengths 3 9 | generation_prompt stringlengths 1.65k 5.15k | joint_prompt sequencelengths 2 2 | paper1_prompt sequencelengths 2 2 | paper2_prompt sequencelengths 2 2 | no_context_prompt sequencelengths 2 2 |
|---|---|---|---|---|---|---|---|---|
[
[
"We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by choo... | 1,524 | 608 | 1524_608 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nWe consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by ... | [
"Paper:\nWe consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by ch... | [
"Paper:\nDifferentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate through forward iteration layers to compute gradients, which couples forward computation and backpropagation and needs to balance ... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"We introduce StyleMorph, a 3D-aware generative model that disentangles 3D shape, camera pose, object appearance, and background appearance for high quality image synthesis. We account for shape variability by morphing a canonical 3D object template, effectively learning a 3D morphable model in an entirely un... | 1,108 | 715 | 1108_715 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nWe introduce StyleMorph, a 3D-aware generative model that disentangles 3D shape, camera pose, object appearance, and background appearance for high quality image synthesis. We account for shape variability by morphing a canonical 3D object template, effectively learning a 3D morphable model in an entirel... | [
"Paper:\nWe introduce StyleMorph, a 3D-aware generative model that disentangles 3D shape, camera pose, object appearance, and background appearance for high quality image synthesis. We account for shape variability by morphing a canonical 3D object template, effectively learning a 3D morphable model in an entirely ... | [
"Paper:\nSelf-supervised learning has significantly improved the performance of many NLP tasks. However, how can self-supervised learning discover useful features, and why is it better than traditional approaches such as probabilistic models are still largely unknown. In this paper, we focus on the context of topic... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Differentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \\emph{group-wise clipping}. To reduce the ... | 717 | 0 | 717_0 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nDifferentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \\emph{group-wise clipping}. To reduce ... | [
"Paper:\nDifferentially private deep learning has recently witnessed advances in computational efficiency and privacy-utility trade-off. We explore whether further improvements along the two axes are possible and provide affirmative answers leveraging two instantiations of \\emph{group-wise clipping}. To reduce th... | [
"Paper:\nThis paper novelly breaks down with ignorable loss an RNN layer into a sequence of simple RNNs, each of which can be further rewritten into a lightweight positional encoding matrix of a self-attention, named the Recurrence Encoding Matrix (REM). Thus, recurrent dynamics introduced by the RNN layer can be e... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Adaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent ascent... | 1,040 | 1,104 | 1040_1104 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nAdaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent as... | [
"Paper:\nAdaptive gradient methods have shown their ability to adjust the stepsizes on the fly in a parameter-agnostic manner, and empirically achieve faster convergence for solving minimization problems. When it comes to nonconvex minimax optimization, however, current convergence analyses of gradient descent asce... | [
"Paper:\nSelf-paced learning has been beneficial for tasks where some initial knowledge is available, such as weakly supervised learning and domain adaptation, to select and order the training sample sequence, from easy to complex. However its applicability remains unexplored in unsupervised learning, whereby the k... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Adversarial patch attacks are an emerging security threat for real world deep learning applications. We present Demasked Smoothing, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against patc... | 1,209 | 549 | 1209_549 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nAdversarial patch attacks are an emerging security threat for real world deep learning applications. We present Demasked Smoothing, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against ... | [
"Paper:\nAdversarial patch attacks are an emerging security threat for real world deep learning applications. We present Demasked Smoothing, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against pa... | [
"Paper:\nMachine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constr... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Our goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be ob... | 122 | 1,226 | 122_1226 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nOur goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can b... | [
"Paper:\nOur goal is to extend the denoising diffusion implicit model (DDIM) to general diffusion models~(DMs) besides isotropic diffusions. Instead of constructing a non-Markov noising process as in the original DDIM, we examine the mechanism of DDIM from a numerical perspective. We discover that the DDIM can be ... | [
"Paper:\nDiffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under ... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"We propose the Gradient-weighted Object Detector Activation Mapping (Grad-ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, Grad-ODAM produces heat maps that show the influenc... | 1,146 | 1,464 | 1146_1464 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nWe propose the Gradient-weighted Object Detector Activation Mapping (Grad-ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, Grad-ODAM produces heat maps that show the infl... | [
"Paper:\nWe propose the Gradient-weighted Object Detector Activation Mapping (Grad-ODAM), a visualized explanation technique for interpreting the predictions of object detectors. Utilizing the gradients of detector targets flowing into the intermediate feature maps, Grad-ODAM produces heat maps that show the influe... | [
"Paper:\nMotivated by the interests of social network analysis and network-based recommendation systems, we consider a semi-supervised community detection problem, where the goal is to estimate the community label of a new node by leveraging on the network structure and partially observed community labels of existi... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Training a robust cooperative agent requires diverse partner agents. However, obtaining those agents is difficult. Previous works aim to learn diverse behaviors by changing the state-action distribution of agents. But, without information about the task's goal, the diversified agents are not guided to find o... | 359 | 1,548 | 359_1548 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nTraining a robust cooperative agent requires diverse partner agents. However, obtaining those agents is difficult. Previous works aim to learn diverse behaviors by changing the state-action distribution of agents. But, without information about the task's goal, the diversified agents are not guided to fi... | [
"Paper:\nTraining a robust cooperative agent requires diverse partner agents. However, obtaining those agents is difficult. Previous works aim to learn diverse behaviors by changing the state-action distribution of agents. But, without information about the task's goal, the diversified agents are not guided to find... | [
"Paper:\nMachine learning models are vulnerable to Out-Of-Distribution (OOD) examples, such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other m... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Dynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often highl... | 1,362 | 27 | 1362_27 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nDynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often h... | [
"Paper:\nDynamic diagnosis is desirable when medical tests are costly or time-consuming. In this work, we use reinforcement learning (RL) to find a dynamic policy that selects lab test panels sequentially based on previous observations, ensuring accurate testing at a low cost. Clinical diagnostic data are often hig... | [
"Paper:\nGradient-based learning algorithms have an implicit \\emph{simplicity bias} which in effect can limit the diversity of predictors being sampled by the learning procedure. This behavior can hinder the transferability of trained models by (i) favoring the learning of simpler but spurious features --- present... | [
"Here's an insight: ",
"It’s established that: "
] |
[
[
"Scene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting cur... | 1,257 | 1,562 | 1257_1562 | You are a helpful AI Assistant that provides well-reasoned and detailed responses. Identify an insight that emerges only when both papers are considered together—something not obvious from either paper alone. You first think about the reasoning process as an internal monologue (1-3 sentences) and then provide the user ... | [
"Paper 1:\nScene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting... | [
"Paper:\nScene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting c... | [
"Paper:\nReal-world machine learning problems often exhibit shifts between the source and target distributions, in which source data does not fully convey the desired behavior on target inputs. Different functions that achieve near-perfect source accuracy can make differing predictions on test inputs, and such ambi... | [
"Here's an insight: ",
"It’s established that: "
] |
End of preview. Expand in Data Studio
README.md exists but content is empty.
- Downloads last month
- 7