Kaist, Emmanuel Bengio, Sungsoo Ahn, Minsu Kim, Jinkyoo Park, Yun Taeyoung
We lifted 17 functions out of this paper's own repositories and ran 9 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dbsxodud-11/ls_gfn | canonical | 9 of 16 |
| dbsxodud-11/ls-gfn | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| batch | Ran | dbsxodud-11/ls_gfn/gflownet/utils.py pointer only (licence: NONE) · get_code("1a1ebe33ee0285e0") |
| collate_states_scores | Ran | dbsxodud-11/ls_gfn/gflownet/policy.py pointer only (licence: NONE) · get_code("e62c1b64d8aa2840") |
| dist_calibration_error | Ran | dbsxodud-11/ls_gfn/gflownet/evaluate.py pointer only (licence: NONE) · get_code("198490929c547443") |
| get_unique_children_in_x | Ran | dbsxodud-11/ls_gfn/gflownet/guide.py pointer only (licence: NONE) · get_code("2dfc42fc19e46728") |
| make_mlp | Ran | dbsxodud-11/ls_gfn/gflownet/network.py pointer only (licence: NONE) · get_code("266dbf4340bef3f5") |
| make_nodesummary_gnn | Ran | dbsxodud-11/ls_gfn/gflownet/network.py pointer only (licence: NONE) · get_code("7e8123dabbedb095") |
| multi_set_distance | Ran | dbsxodud-11/ls_gfn/gflownet/monitor.py pointer only (licence: NONE) · get_code("6dd46344ca1baf43") |
| pack | Ran | dbsxodud-11/ls_gfn/gflownet/utils.py pointer only (licence: NONE) · get_code("1c23da6bab23ab31") |
| tensor_to_np | Ran | dbsxodud-11/ls_gfn/gflownet/utils.py pointer only (licence: NONE) · get_code("973c7b0697f6a623") |
| BaseTBGFlowNet | Not yet run | dbsxodud-11/ls-gfn/gflownet/GFNs/basegfn.py pointer only (licence: NONE) · get_code("23d514ace7e9b0fb") |
| anderson_darling | Not yet run | dbsxodud-11/ls_gfn/gflownet/evaluate.py pointer only (licence: NONE) · get_code("cfafc9ffa57d7acf") |
| collate_probs | Not yet run | dbsxodud-11/ls_gfn/gflownet/policy.py pointer only (licence: NONE) · get_code("797497ec7c51c286") |
| guide_logp | Not yet run | dbsxodud-11/ls_gfn/gflownet/guide.py pointer only (licence: NONE) · get_code("ac05e44262397b23") |
| guide_sample | Not yet run | dbsxodud-11/ls_gfn/gflownet/guide.py pointer only (licence: NONE) · get_code("6b1b2290357fc6dc") |
| make_convnet | Not yet run | dbsxodud-11/ls_gfn/gflownet/network.py pointer only (licence: NONE) · get_code("b3c76e81a3360fbd") |
| make_full_exp | Not yet run | dbsxodud-11/ls_gfn/gflownet/data.py pointer only (licence: NONE) · get_code("07d2630a9cb727da") |
| unique_keep_order_filter_children | Not yet run | dbsxodud-11/ls_gfn/gflownet/policy.py pointer only (licence: NONE) · get_code("f928c1f4da5deecd") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse samples, yet occasionally struggle to consistently produce samples with high rewards due to over-exploration on wide sample space. This paper proposes to train GFlowNets with local search, which focuses on exploiting high-rewarded sample space to resolve this issue. Our main idea is to explore the local neighborhood via backtracking and reconstruction guided by backward and forward policies, respectively. This allows biasing the samples toward high-reward solutions, which is not possible for a typical GFlowNet solution generation scheme, which uses the forward policy to generate the solution from scratch. Extensive experiments demonstrate a remarkable performance improvement in several biochemical tasks. Source code is available: https://github.com/dbsxodud-11/ls_gfn.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.02710")
get_code_for_paper("2310.02710")
have("2310.02710")
Connect an agent — have() is free.