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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
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abstract
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.
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- abstract In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed
- method To validate whether the quality advantage of GS scenes translates to stronger navigation agents, we train five agent groups under differ- ent scene-domain mixtures, with training budget fixed at5×107 steps:A: 100 mesh scenes,B: 100 GS scenes,C: 80M + 20G,D: 50M + 50G, andE: 20M + 80G. All agents share a unified DD-PPO [30] architecture with a ResNet [6] visual encoder and a GRU [4] policy head, receiving256×256RGB and depth observations, with only training scene composition varying. Each agent i
- method Recent works [54,62] have demon- strated that incorporating G-buffers and temporal cues can significantly improve reconstruction quality. Motivated by this, we propose a Geometry-Temporal Re- current Refinement Network to further enhance the interpolated outputs. As illustrated in Fig. 3 (a), the proposed network employs a modified gated recurrent unit (GRU) [9] architecture. The network takes as input the current 8 Y. Zhao et al. low-resolution frameIand its gradientsG, the depth mapD, the norm
- method and we map the stateH t to queries, keys and values with affine projections using learned parameter matrices W Q∈ Rd×d/k, W K∈ Rd×d/k, W V∈ Rd×d/k and W O∈ Rd×d. At step t, the UT then computes revised representationsH t∈ Rm×d for all m input positions as follows H t = LAYERNORM(At+TRANSITION (At)) (4) where At = LAYERNORM((H t−1+P t)+ MULTIHEAD SELFATTENTION (H t−1+P t)), (5) where LAYERNORM () is defined in Ba et al. (2016), and TRANSITION () and P t are discussed below. Depending on the task, w
- dataset and the kinematics are deterministic; digits overlap without collision, and bouncing reflections are non-linear. ( 2)PhyWorld Collision 30K[Kang et al., 2025] comprises roughly 30 thousand simulated rigid-body collisions with varying ball radii and velocities. It tests OOD generalisation and strict physical consistency (momentum and kinetic energy conservation). (3)WeatherBench (2m temperature)[Rasp et al., 2020] is a processed version of the ERA5 archive [Hersbach et al., 2020], containing glob
- background Middle: attention-based learning enables adaptive and reliability-aware aggregation of heterogeneous measurements. Right: representative applications, including radio map reconstruction, LEO satellite localization, and map-informed resource allocation. To address these challenges, attention mechanisms have emerged as an effective framework for adaptive information aggregation. Originally developed for neural machine trans- lation [29], [30] and later generalized by Transformers [31], attention c
- method θm (zt, at)→ˆz t+1) predicts the next latent state after a primitive action, and the decoder (Decθd (ˆzt+1)→ˆs t+1) reconstructs next-state information from the predicted latent. The pur- pose is not to use this model as a long-horizon planner, but to learn a dynamics-aware latent space where behaviorally similar states can merge and useful hubs can emerge. We use a GRU [9] memory module with clipped history so the latent can capture recent context without memorizing full trajectory identity. Ad
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